Strategic Groups and Collaboration
Master ThesisMSc. Business Administration – Strategic Innovation Management July 2018
Supervisor: Dr. Charles Carroll Co-Assessor: Dr. Pedro de Faria
Laura Kratschmann S2412713
Abstract
Historically speaking, strategic management dominantly focused on competition and its effect on firm performance. This predominant focus on competition might explain why in comparison strategic group collaboration is rather underresearched. Therefore, this study examines the collaborative relationships that might emerge within strategic groups. This paper links group-level effects with a strategic alliance perspective to develop a theoretical foundation on why and how strategic group members might collaborate. In order to identify strategic groups a cluster analysis was conducted with a sample of 24 automotive firms. The identified clusters were then compared with alliances formed within the automotive industry. Additionally, a MANOVA analysis was performed to compare the performance between strategic groups. The analysis found that the automotive industry is divided into four strategic groups. Furthermore, this study revealed that to the greatest extent strategic groups reflect the formation of production alliances. Strategic group members pool their resources and capabilities in an effort to achieve economies of scale. Yet, the results did not confirm that the formation of different types of alliances also led to differences in group performance.
Key words: Strategic groups, group-level effects, collaboration, interaction, alliances, performance
1. Introduction
Strategic groups are an important aspect of an industry structure that enable researchers and practitioners to analyze and comprehend complex industry dynamics (McGee & Thomas, 1986; Mas-Ruiz et al., 2014). The concept of strategic groups was first introduced in 1972 by Hunt and has been defined as: a set of firms that are homogenous within and heterogeneous between groups in terms of strategy (Barney & Hoskisson, 1990). The concept has combined two research streams: industrial organization and strategic management. These two perspectives provide competing assumptions. Industrial organization proposes that an industry is composed of homogenous firms while strategic management proposes that an industry is composed of heterogeneous firms that are completely distinct from one another (Murthi et al. 2013). In contrast, the strategic group approach allows both assumptions to co-exist and flourish (Porter, 1980). Thus, the conceptualization of strategic groups has been important in filling the gap between industry and firm level analysis. Therefore, strategic groups provide an intermediate unit of analysis, also described as group analysis (McGee & Thomas, 1986; Sudharshan et al., 1991). This intermediate level of analysis aims to explain the structure of an industry through strategic groups. In addition, it aims to establish a link between group membership and firm performance (Rumelt, 1991).
Ruiz-Moreno, 2011; Epure et al. 2011). Ergo, strategic groups are presumed to reflect the competitive structure within an industry (Tang & Thomas, 1992).
The suggested link between strategic groups and competition has led to a well-known debate in literature; precisely to which extent strategic groups reflect patterns of competition (Cattani, et al., 2017). This debate is driven by the investigation of whether rivalry is greater between or within strategic groups, however with inconclusive results (Cool and Dierickx, 1993; Mas-Ruiz and Ruiz-Moreno, 2011). On top of that, it is emphasized that instead of comparing between and within group relationships, research should be directed towards within group behavior instead (Smith et al., 1997). Around the same time, little attempt has been made by authors to examine and understand the collaborative relationships within strategic groups. However, understanding collaborative relationships is crucial, especially since collaboration might heavily influence the performance of firms (Nohria and Garcia-Pont, 1991).
Thus, rivalry among strategic group members is an important research stream, but so is collaboration. In fact, collaborating with other group members might generate great benefits, including defending their position from competing groups (Nohria and Garcia, 1991). For this reason, the paper aims to contribute to strategic group research by examining the collaborative relationships between strategic group members with the following research question:
How are strategic groups related to patterns of collaboration?
The strategic group concept is especially relevant to managers, as it improves their ability to comprehend the industry dynamics their firm is affected by (McGee & Thomas, 1986). Strategic group analysis enables managers to identify direct rivals and thereby partners for beneficial relationships. Thus, literature needs to develop a theoretical understanding of strategic groups, its implications on collaboration and performance. This is especially important because managers can use this tool in an effort to develop competitive and collaborative strategy with greater certainty about its benefits and implications on performance. Ultimately, understanding the tensions between competition and collaboration within strategic groups enables firms to improve their market position, and consequently overall performance. This demonstrates the value of such an analysis and thus, it becomes critical to first understand the underlying mechanisms of strategic groups, as well as its implication on strategic group collaboration.
2. Literature Review
2.1. Strategic Group Concept
Hunt (1972) was the first to study strategic groups and defined them as groups in which firms follow homogenous strategies within and heterogeneous between strategic groups (McGee & Thomas, 1986). Furthermore, in the relation to strategic groups Caves and Porter (1977) shaped the theory of mobility barriers. Mobility barriers describe entry and exit barriers. These barriers are group specific, which protect groups from external firms entering the group but also restrict group members from exiting the group (Hatten & Hatten, 1987). Yet, it is important to recognize that while such barriers might exist they do not necessarily have to exist (Carroll, 2018). Mobility barriers can be described as structural factors that prevent the movement of firms unless the firms are willing to incur significant cost in order to overcome them (Harrigan, 1985; Hatten & Hatten, 1987). The strategic group concept has been widely used in order to analyze the competitive structure of complex industries (Barney and Hoskisson, 1990). Additionally, this concept has frequently been employed to examine the origin of firm performance by analyzing the industry on the group-level (Cattani et al., 2017). The debate on performance sparked the greatest interest among theorists and researchers (Dranove et al., 1998). However, the concept has also come with its own range of controversies.
One of these controversies is “infinite dimensionality”, a term introduced by Cattani et al. (2017). It describes the problem between the infinite similarity and differences firms can be classified on. Solely by changing the attribute in question firms can become more or less similar. Therefore, the correct choice of attributes is of utmost importance (Murphy & Medin, 1985). In fact, this might have led to non-findings in strategic group research (Ketchen & Shook, 1996). Thus, it is crucial to examine the industry to be studied in order to guarantee that the variables selected reflect the competitive characteristics of the industry.
scholars to questioned whether these are distinct groups or purely a result out of statistical convenience. Consequently, scholars questioned how meaningful the results of the analysis are and if strategic groups actually exist (Barney & Hokisson, 1990). Fortunately, a significant test for cluster analysis has emerged from the fields of biology and ecology that might enable researchers to rectify this issue (Carroll, 2018). Over time, two views on strategic groups have emerged: the independent and interdependent view (Carroll, 2018). These views were introduced in an effort to explain possible non-findings in strategic group literature. The independent view defines strategic groups as firms that follow similar strategies but are not competing against one another (Hatten & Hatten, 1987). In this scenario, the strategic groups are artifacts of the method instead of meaningful groupings (Carroll, 2018). While this is not the ideal scenario, it might still help to simplify an otherwise complex industry into smaller sub groups of similar firms. Thus, this scenario assists by making an industry easier to analyze and might still aid in mapping future strategies (Hatten & Hatten, 1987).
2.2. Interactions within Strategic Groups
Firms across all industries are driven by acquiring the best strategic position available in an industry to ultimately outperform competitors (Short et al., 2007). Accordingly, countless literature revolves around the quest to understand the competitive dynamics of industries and its performance implications (Chen & Miller, 2012; Garud & Kumaraswamy, 1993). With this goal in mind, strategic group scholars frequently employ the structure-conduct-performance (SCP) paradigm in an effort to understand and explain industry dynamics (Smith et al., 1997). In industrial organization (IO) the SCP paradigm was created to contextualize the competitive landscape of an industry and to eventually explain firm performance (Panagiotou, 2006).
In the SCP paradigm strategic groups reflect the “structure” component of the paradigm, providing an intermediate level of analysis of an industry, the intra-industry structure. In essence, the paradigm proposes that the underlying structure of an industry shapes the conduct of firms, which in turn determines performance of the firms within the industry (Bain, 1956; Lipczynski and Wilson, 2004). Thus, strategic groups shape the way firms interact, which then leads to particular performance outcomes. However, research had continuously inferred a stable relationship between all components of the SCP (Panagiotou, 2006). This particularly assumption has led strategic group scholars to eliminate the important conduct component from the equation. Instead scholars began investigating the structure-performance link. This approach originated from one particular assumption: transitivity. If A determines B and B determines C, A must also determine C. This logic was transferred to the strategic group concept. Structure (e.g. strategic groups) determines conduct, which in turn determines performance. This implies that strategic groups, their conduct and performance must have a transitive relationship. Thus, strategic groups must determine performance (Caves and Porter, 1978). However, this assumption has come under harsh criticism (Phillips, 1976; Clarke, 1985; Carroll, 2017).
structure-performance link is applicable only under two conditions: perfect competition and monopoly (Porter, 1979). This entails that conduct within strategic groups can fluctuate quickly and frequently while the structure remains the same. This leads to the effect that while the structure does not change conduct does, which may significantly change the performance outcome of these firms. Consequently, a structure-performance link is not applicable to strategic group research (Carroll, 2017). Conduct needs to be central to strategic group literature (Dranove et al., 1998). Thus, the SCP paradigm shows how important conduct is when analyzing strategic groups and that it should not be excluded.
As time progressed, strategic group scholars endorsed the importance of the original SCP paradigm instead of the short cut (Panagiotou, 2006). Accordingly, research moved towards the subject of conduct, specifically, if and how strategic groups reflect patterns of competition (Cattani, et al., 2017). Subsequently, this debate brought rivalry to the forefront of the strategic group discussion. On the one hand, authors propose that rivalry is stronger within a strategic group. Firms of the same group possess homogenous resources therefore they will most likely need to acquire similar inputs as well as pursue the same kind of customers (Hatten and Hatten, 1987). On the other hand, authors argue that similarity reduces direct competition. Since firms pursue and consequently fight over similar inputs and customers, firms will realize their mutual dependence. And once firms recognize this interdependence, they are inclined to cooperate or collude with one another (Caves and Porter, 1977; Peteraf, 1993). Consequently, exhibiting theoretical controversy on the relationship between similarity and rivalry (Cool & Dierickx, 1993; Simth et al. 1997; Mas-Ruiz et al. 2005). This debate follows the idea that competition and cooperation between firms are direct opposite, inferring they will not occur at the same time. However, coopetition literature has found this assumption to be flawed (Brandenburger and Nalebuff, 1996).
they most likely cooperate at the same time. Cooperative actions among strategic group members may range from passive to active types of cooperation. Passive cooperation might be displayed by mutual forbearance, in which firms simply agree not to compete in certain markets or on strategic dimensions. On the contrary, active cooperation might reach up until collective strategies where strategic group members work together towards a common goal (Carroll, Porac and Thomas, 1993). Carroll, Porac and Thomas (1993) also describe this as the strong definition of strategic groups, which elevates industry analysis by examining more “sophisticated strategic behavior” (p. 10). These collective strategies, which in essence embody joint actions and collaborations (Astley, 1984; Gomes-Casseres, 2003) is what this research paper is particularly interested in. Carroll, Porac and Thomas (1993) have described joint action as possibly one of the most interesting research streams for strategic groups. For instance, strategic groups pursuing collective strategies (e.g. collaboration) might be able to manipulate the structure of the industry to their advantage (Carroll, Porac and Thomas, 1993). Furthermore, firms pursue collective strategies in order to differentiate themselves from other competitive groups, which ultimately determine their share of profits within the industry (Gomes-Casseres, 2003). Collective strategies may also be pursued in order to manage horizontal interdependencies (Astley & Fombrun, 1983). Consequently, formulating collective strategy provides a powerful tool for strategic group members and this is why it is crucial to study these particular joint actions more thoroughly.
linkages will have significant performance implications for firms, yet the authors did not include any performance variables to support the argument. Thus, the study has a greater focus on investigating networks of firms with collaborative agreements while this study aims to investigate the collaborative patterns within strategic groups. Furthermore, this study will implement performance variables in order to examine the performance implications between strategic groups.
2.3. Collaboration and Group-Level Effects
Dranove et al. (1998) suggests that the aggregation of firms with similar strategies can achieve true level effects on performance. According to the scholars, group-level effects are a direct consequence of group member interaction. Furthermore, these particular effects are more then just the sum of firm-level effects. These group-level effects induce firms within the group to alter their behavior in order to take advantage of these effects. Hence, group members’ behavior changes in the presence of strategic groups in comparison to the absence of a group. In other words, in the presence of strategic groups, members within the same group will be expected to cooperate in order to achieve true group-level effects. The authors defined three types of true group-level effects: market power, efficiency and differentiation.
The next paragraph will describe each group-level effect individually and hypothesize which type of collaborative agreement (e.g. strategic alliance) strategic group members will most likely engage in, in order to achieve the different types of group-level effects. It is important to remember, that the following section does not imply that collaborative relationships between strategic groups never emerge but rather provide a theoretical foundation in order to understand why and how within group collaboration might emerge. Thus, the following section will focus on within group collaboration.
Market Power
monopoly position (Scherer and Ross, 1990). In fact, firms attempting to collude might choose any strategic dimension relevant to competition in a given market (Jacquemin & Slade, 1989). Such collusion among direct competitors is used as a means to undercut competitive behavior and ultimately accomplish joint profit maximization (Baumol, 1992). Cartel is a term often used to refer to groups of firms that collude and coordinate their behavior (Jacquemin & Slade, 1989). Cartels are contractual like agreements in which competitors agree to act as if they were one unit. This enables a group of firms to occupy a monopoly position within a market (Jacquemin & Slade, 1989). Due to the illegal nature of collusion and therefore of cartels, firms have incentives to hid such behavior, making them difficult to detect altogether. Nevertheless, due to the benefits derived from coordinating their behavior, such as increased profit margins, they often continue to collude whenever possible (Levenstein & Suslow, 2012).
One collaborative agreement that shares some cartel like characteristics is a joint venture (Park & Russo, 1996). A joint venture is defined as a legal organization established by two or more firms. The nature of joint ventures requires continuous cooperation and information exchange, allowing firms to coordinate their behavior with greater ease (Pfeffer and Nowak, 1976; Barney, 2002). Joint ventures are typically managed collectively. Thus, intimate interaction and communication between the partnering firms are required, including regular meetings. This continuous contact enables partner firms to coordinate prices, output or on any other strategic decision (Jacquemin & Slade, 1989). Essentially, joint ventures allow competing firms to share any type of information that can be used for informal agreements on anticompetitive actions. Furthermore, Brodley (1982) stated, that joint ventures might aid in enforcing the ‘cartel law’. Participating firms might punish firms who fail to comply with the agreed terms by refusing to cooperate on essential activities for joint venture success.
Considering the fact that strategic groups members are expected to offer similar products and hence are direct competitors (DeSarbo et al., 2008), forming intra joint ventures and with that forming collusive agreements will be most beneficial within strategic groups. As a result, strategic groups members might form joint ventures in an effort to facilitate collusion and with that achieve market power.
Efficiency
Efficiency relates to the degree to which the lowest amount of input produces the highest amount of output (Hambrick, 1983). Firms that show the highest levels of efficiency are usually linked to possessing economies of scale and scope (Baumol, 1967). For most firms achieving efficiency is an important objective especially when resources are scarce and/or costly, with firms even forming alliances with competitors to achieve such crucial efficiencies (Dranove et al. 1998). These alliances are also referred to as “scale alliances”, in which firms pool their similar resources and capabilities in order to achieve higher efficiencies (Walley, 2007) or reduce excess capacity (Mitchell et al., 2002). For example, firms might form production alliances with other firms to reach efficient production size, and with that scale advantages and related efficiencies (Dranove et al. 1998).
Differentiation
Differentiation refers to a firms’ focus on distinguishing their products from those of their rivals. Firms that aim to achieve this might focus on differentiation through R&D (David et al., 2002; Hambrick, 1983). Firms investing in R&D aim to differentiate themselves by offering innovative products with superior quality that are unlike any other products on the market (Banker et al. 2014; Fernando et al., 2016). Thus, differentiation through R&D is the creation of physical differences that are meaningful to consumers, thereby distinguishing the firms’ offerings from a rivals offering (Kotler & Armstrong, 2003). Following this logic, strategic groups can differentiate themselves form other groups by developing new products. Thus, joint R&D efforts lead to homogenous products within groups while heterogeneous products between groups.
According to Porter (1990), innovation is the only way for competitors to gain sustainable competitive advantage due to developing unique qualities desirable to customers along with the possibility to command a price premium on these products (Banker et al., 2014). Furthermore, through innovation firms create barriers of entry (Porter, 1980) which results in reduced competition (Hotelling, 1990). Therefore, innovation might grant firms to improve their competitive position. Innovation efforts have seen a great shift from solely focusing on internal R&D to also sourcing external R&D (Linder et al, 2003). Firms are increasingly faced with heightened competition across all industries, which forces firms to open up their R&D departments This is a direct result of firms having to compete on “shorter product life cycles, faster product renewal and increasing R&D costs” (Berchicci, 2013, p. 117). Thus, leveraging external sources, along with internal R&D, has become a critical component in maintaining and enhancing competitive advantage (Doz et al., 2000). In the context of this research, continuous R&D investments might enable strategic groups to sustain a collective competitive advantage over other groups.
current products (Belderbos et al., 2004) as well as develop new products (Quintana-Garcia and Benavides-Velasco, 2004; Tether, 2002). While there has been debate about the most effective strategy among competitors, incremental product improvement or radically new products, historically competitors have pursued both (Belderbos et al., 2004; Tether, 2002). As a result, strategic group members might form R&D alliances within groups in order to improve current products or develop new products that will differentiate them from other strategic groups.
Performance
As discussed in earlier sections, performance implications on the group and firm level have been an important research stream in strategic group literature and have been examined by many scholars (e.g. Cool & Schendel, 1987; Peteraf & Shanley, 1997; Hatten & Hatten, 1987). Moreover, these performance implications of groups have been typically examined through financial performance variables. Historically speaking, strategic management dominantly focused on competition and its effect on firm performance (Astley, 1984). This focus can still be observed in strategic group literature today. This might be the reason why also in strategic groups there has been a greater emphasis on competitive instead of collaborative relationships. However, just like competition, collaborative agreements between group members will influence individual firm performance (Gomes-Casseres, 2003; Nohria and Garcia-Pont, 1991).
and its members. Nohria and Garcia-Pont (1991) for example argued that within group collaboration might highly contribute firms’ performance outcomes. Moreover, overall firms form collaborative agreements with competitors to benefit from them and ultimately improve the performance (Brandenburger and Nalebuff, 1996). Thus, the assumption is made that collaborative agreements between members of the same group will have positive performance implications on the group as a whole.
H1: Strategic groups that engage in within group collaboration have a higher level of performance than groups that engage in between group collaboration.
3. Methodology
3.1. Industry Setting
This research paper examined the research question in a specific industry setting, the automotive industry. The characteristics of the automobile industry make it an ideal case for this analysis. Firstly, automotive manufactures face similar market dynamics on the supply and demand side (Nohria and Garcia-Pont, 1991). Secondly it is widely known for firms forming different strategic alliances (Womack, 1988; Ohmae, 1989), especially entering into a variety of coopetition relationships (Bengtsson et al. 2010; Lacoste, 2012). Furthermore, the industry is regarded as one of the most globalized industries (Sturgeon et al., 2008; Dicken, 2015). Therefore, the hypotheses were tested on global automotive firms.
3.2. Data Collection
the Orbis database in order to search for automotive firms. In Orbis the year 2013 was selected. This year was selected because of the available dataset from only that exact year. Moreover, I filtered the results to include only the conglomerate automotive corporations. I specifically ensured that there is enough overlap between the firms from Orbis and the firms included in the alliance dataset. The first step of filtering was performed because Orbis provided data on firms that only manufactured individual parts. Once this step was completed, I selected the appropriate strategic variables in Orbis. The firms left, were firms that provided information on all selected strategic variables. One strategic variable, “sales, general and administrative expenses”, selected was not available in Orbis. However, this variable was available in COMPUSTAT. Thus, this variable was individually retrieved from COMPUSTAT for the previously selected firms in Orbis. Through this process, I ended up at a final sample size of 24 automotive firms (Table 1). While this sample size is relatively low, the alliance dataset matches, a critical factor in order to analyze the hypotheses. Table 1: Overview of Firms per Strategic Group
Strategic Group Firm Strategic Group Firm 1 VOLKSWAGEN 3 FORD 1 TOYOTA 3 FIATCHRYSLER 2 DAIMLER 3 SAIC 2 GM 3 PEUGEOT 2 HONDA 3 KIA 2 BMW 3 TATA 2 NISSAN 3 MITSUBISHI 2 RENAULT 3 GREATWALL 2 MAZDA 3 GEELY 2 SUZUKI 3 FUJI 2 DONGFENG 4 HYUNDAI 2 ISUZU 2 YAMAHA 3.3. Strategic Variables
intensive and requires a lot of investment in assets (e.g. facilities, manufacturing and materials) (Chiaroni et al. 2011). This has lead to the continuous quest of firms to find new ways to reduce cost in materials and production as well as achieving economies of scope and scale (Freyssenet and Lung, 2000).
Moreover, firms continuously aim to reduce their inventory levels and the related cost by achieving Just-In-Time delivery (Larsson, 2002). The automotive industry is always changing due to rapid technology developments in addition to changing customer preference. This has promoted a high focus on innovation in order to incorporate new technologies and satisfy rapidly changing consumer preferences (Lavie, 2006). Moreover, automotive firms invest in marketing strategies to ensure consumers perceive the differences among products and to gain a favorable position in the mind of the consumer (Tay, 2003). These industry characteristics lead to the identification of 6 variables that also have been found in other studies to capture similar characteristics in other industries (e.g. capital intensive and R&D intensive). Thus, to capture the essence of these competitive characteristics these six variables were considered. The following section will briefly describe the selected strategic variables.
Number of employees: In the automotive industry size reflect the extent to which the firm has economies of scale, which is an important factor in the auto industry (Nohria and Garcia-Pont, 1991). Size is operationalized through number of employees.
R&D intensity: The automotive industry environment changes rapidly and firms try to adjust and stay ahead of these forces through innovation (Lavie, 2006). Innovation is operationalized by R&D intensity.
Total Assets: The automotive industry is asset-intensive (Chiaroni et al., 2011), hence the asset endowments of the manufacturing firms is of crucial importance. Furthermore, this measures also relates to the size of a firm and therefore its tendency to achieve economies of scale (Nohria and Garcia-Pont, 1991).
Inventory levels: JIT is becoming more crucial for automotive firms. Through this practice firms are able to reduce significant inventory levels and cost (Larsson, 2002). Inventory level will be used in order to operationalize how well firms achieve JIT delivery.
Capital expenditures: The automotive industry is a capital-intensive industry (Chiaroni et al., 2011), which requires large amounts of investments in order to stay competitive. This competitive requirement is operationalized by capital expenditures.
3.4. Dependent Variables
The dependent variables employed in this research paper reflect financial performance variables. In total three variables were selected: (1)return on assets, (2)return on equity and (3)profit margin.
3.5. Collaboration Variables
3.6. Analysis
Strategic groups were identified through a hierarchical cluster analysis realized in SPSS. More precisely Ward’s hierarchical cluster analysis and Euclidean distance were applied. Furthermore, a significance test was performed. This significant test aids in determining whether the cluster analysis has found distinct groupings (Carroll, 2018). Furthermore, after conducting a Pearson correlation test on the six strategic variables, due to a high correlation, three variables were left for the cluster analysis. 1)selling, general and administration expenses, 2)R&D intensity and 3)assets (Table 2 and 3). A strong correlation between strategic variables, also called collinearity, is problematic. This strong correlation implies that the variables are more likely to represent the same concept (Sambandam, 2013). Thus, highly correlated variables were disregarded and the cluster analysis was continued with these three variables. Table 2: Pearson Correlation (n=24)
Strategic Variables 1 2 3 4 5 6 1. SGA Expenses 1 -0.078 -0.353 0.035 0.209 -0.046 2. Employees -0.078 1 0.192 0.913** 0.886** 0.940** 3. R&D Intensity -0.353 0.192 1 0.198 0.055 0.224 4. Assets 0.035 0.913** 0.198 1 0.918** 0.910** 5. CAPEX 0.209 0.886** 0.055 0.918** 1 0.897** 6. Inventory -0.046 0.940** 0.224 0.910** 0.897** 1
Note: * significant at p<0.05; ** significant at p<0.01 Table 3: Pearson Correlation (n=24)
Strategic Variables 1 2 3
1. R&D Intensity 1 -0.353 0.198
2. SGA Expenses -0.353 1 0.035
Table 4: Descriptive Statistics
Strategic Variables Mean St.D. Maximum Minimum
SGA Expenses -7,045E-5 0.977 -4.39034 4.06532
R&D Intensity 0.045 0.893 -1.49805 1.59322
Assets -0.005 0.980 -4.37706 3.94040
The second step included examining the agglomeration schedule in order to create a scree plot that aids in identifying the elbows. The agglomeration schedule enables the researcher to examine the changes of the agglomeration coefficients, which are essentially used to determine the appropriate cluster solution. This study is taking an exploratory approach to examining how strategic groups collaborate. Thus, to examine the alliances within strategic groups a frequency table was created. The table outlines the within and between group alliances observed in the automotive industry in year 2013. Furthermore, to examine the hypothesis H1 a MANOVA analysis was performed. This test will measure if the performance differences between firms that are forming within group alliances and between groups alliances are significantly different. As previously stated, the performance variables selected for the analysis are: (1)return on assets, (2)return on equity and (3)profit margin.
4. Findings and Analysis
4.1. Strategic Groups
Table 5: Significance Test per Cluster Solution
Number of Clusters Ward’s Criteria Simulation Test
12 1.323 0.01 11 1.556 0.01 10 1.828 0.01 9 2.500 0.01 8 3.491 0.01 7 5.172 0.01 6 7.158 0.01 5 10.185 0.01 4 14.259 0.02 3 27.126 0.49 2 46.040 0.80 Note: significant at p<0.05
Figure 2: Scatter Plot of Strategic Group Solution 4 Table 6: Overview of Strategic Variables average per group
Strategic Groups SGA R&D Intensity Assets Group 1 715.407 0,0364 360.861 Group 2 298.032 0,0366 80.134 Group 3 1.344.339 0,0127 47.261 Group 4 16.853.721 0,0083 126.418
Strategic Group 1 – Big Players
Strategic Group 2 - Innovators
Strategic group two consists of 11 firms making this group the largest one out of the four. These firms exhibit the highest R&D intensity. Thus, this group has the heaviest focus on R&D activities and staying ahead of competition by introducing new and improved products. In comparison to the other groups this group exhibits the lowest level of SGA expenses and have the second lowest score of total asset. Thus, Group 2 is less concerned with differentiation in terms of marketing. Furthermore, this group might lack the asset requirements of an asset intensive industry in comparison to other groups.
Strategic Group 3 – Small Marketer
Strategic group three consists of ten firms. These firms do not exhibit a maximum score in any strategy. However, they are the second highest SGA spenders among the other groups. In comparison to the other groups this group has the lowest score of total assets. Additionally, this group has the second lowest score on R&D intensity. Thus, Group 3 is rather fragmented in terms of their strategy. This group is following a differentiation strategy through investing in both innovation and marketing activities.
Strategic Group 4 – Big Marketer
Strategic group four consists of only one firm. This firm exhibits by far the highest score of SGA expenses. However, in comparison to the other groups this group has the lowest R&D intensity score. As a result, this group seems to focus less on innovation in an effort to differentiate and sustain competitive advantage from other groups. Thus, this group instead focuses on establishing a differentiated positing through selling and marketing their products differently. Furthermore, this group also has the second highest score on total assets.
4.2. Strategic Group Collaboration
Table 7: Strategic Groups Total Alliance Overview
Big Players Innovators Small Marketer Big Marketer Total
Big Players 0 4 6 0 10
Innovators 2 31 17 0 50
Small Marketer 4 16 16 0 36
Big Marketer 0 0 0 0 0
Table 8: Strategic Groups R&D Alliance Overview
Big Players Innovators Small Marketer Big Marketer Total
Big Players 0 4 1 0 5
Innovators 2 13 9 0 24
Small Marketer 1 6 6 0 13
Big Marketer 0 0 0 0 0
Total 42
Table 9: Strategic Groups Joint Venture Overview
Big Players Innovators Small Marketer Big Marketer Total
Big Players 0 0 2 0 2
Innovators 0 2 2 0 4
Small Marketer 2 4 5 0 11
Big Marketer 0 0 0 0 0
Total 17
Table 10: Strategic Groups Co-Production Alliance Overview
Big Players Innovators Small Marketer Big Marketer Total
Big Players 0 0 3 0 3
Innovators 0 16 6 0 22
Small Marketer 1 6 5 0 12
Big Marketer 0 0 0 0 0
In total the firms from the sample formed 96 alliances. Out of these alliances 17 were joint ventures, 37 were co-production alliances and 42 were R&D alliances. Furthermore, out of the total 96 alliances formed, 47 were within group alliances while 49 were between group alliances. This number is not distinctively different and based on this number automotive firms tend to form within and between group alliances in almost equal numbers. Thus, while firms formed in total more between group alliances the difference is not substantially larger. However, when looking at the different types of alliance formed within or between groups’ differences emerge. The next section will describe this in more detail along with the hypothesis.
Market power was concerned with the formation of joint ventures within groups, however the results show that firms do not form more joint ventures within groups than between groups. In the sample ten joint ventures were formed between groups, but only seven within groups.
Efficiency was concerned with the formation of within group co-production alliances. In the sample 16 co-production alliances were formed between groups and 21 alliances were formed within groups in total. This manifests that overall more co-production alliances were formed within groups. Moreover, this result displays the largest difference between within and between group alliances.
Differentiation was concerned with the formation of within group R&D alliances, however the results show, that firms do not form more within group R&D alliances. In the sample 23 R&D alliances were formed between groups, but only 19 within groups. These results show, that in total firms do form a fair amount of R&D alliances to develop innovations. However, firms do not form more within group R&D alliances versus between group R&D alliances.
4.3. Strategic Group Performance
strategic groups. In this scenario, the test exhibits strategic groups that form more within group alliances than strategic groups that form more between group alliances, do not significantly differ in terms of performance. H1 cannot be confirmed.
Table 11. Lavene’s Test of Equality of Error Variances
F df1 df2 Sig. ROE 1,163 3 20 0,348 ROA 1,132 3 20 0,360 Profit Margin 1,105 3 20 0,370 Table 12. MANOVA Value F h. df e. df Sig. Pillai’s Trace 0,235 0,568 9,000 60,000 0,818 Wilks’ Lambda 0,770 0,553 9,000 43,958 0,828 Hotelling’s Trace 0,291 0,538 9,000 50,000 0,840
Roy’s Largest Root 0,262 1,747e 3,000 20,000 0,190
5. Discussion & Conclusion
The findings demonstrate that automotive firms form more between than within group joint ventures. Overall, the results illustrate that automotive firms form fewer joint ventures in comparison to other types of alliances. The automotive industry is a highly concentrated industry (Humphrey & Memedovic, 2003) and joint ventures between competitors grant strongest antitrust scrutiny (Baker, 1994). These two factors are a strong indicator that automotive firms might be under closer antitrust investigation when forming joint ventures. Interestingly, strategic group “Small Marketer” formed in total the most joint ventures while also forming the highest joint ventures within group than between groups. This might indicate that this group is trying to collude in an effort to achieve a monopoly position. Firms that have a focus on marketing do not focus on price premiums via unique products but rather through marketing messages. Thus, these firms might collude on prices within groups yet still focus on differentiating their group and products from others. However, overall due to the results automotive firms might be less likely to form joint ventures in an effort coordinate their behavior.
costs. Furthermore, this reduces the cost of investments in production facilities as well as manufacturing equipment. Thereby, creating a win-win situation.
Moreover, findings depict that more between group R&D alliances were formed in comparison to within group R&D alliances, with a difference of four alliances. This might be a direct result of the nature of the automotive industry. The automotive industry is a mature and highly competitive industry, which requires firms to rapidly and frequently innovate (Lavie, 2006). Especially, the strategic group that has a high focus on R&D followed the most R&D alliances, underlying their focus on innovation even more. This increasing dependence on R&D in the automotive industry to sustain competitive advantage might require firms to implement more differentiated external sourcing strategies despite group-level advantages. This might also explain why R&D alliances are formed the most in comparison to any other alliances. Furthermore, the formed alliances are more dispersed than other alliances (Table 8). Duysters & Lokshin, (2011), have argued that a more diversified portfolio of different partners put firms in a better position to achieve and sustain innovation. Ergo, diversity of partners is the main source of firm’s innovativeness. Thus, the question regarding R&D may not entail if within or between group alliances are more beneficial or formed. But, instead strategic group members need to follow both with similar intensity. This improves the diversity of the partner portfolio, which is crucial to increase innovation output.
group alliances. Yet, performance of all groups might be similar. Collaboration might equally end in enhanced performance as it may end up in failure and increased competition.
While many researchers argue for the financial benefits coopetition might bring (e.g., Amaldoss et al. 2000; Brandenberger and Nalebuff 1996; Luo, Slotegraaf, and Pan 2006; Sheth and Sisodia 1999) others stress the risk of such relationships (Park and Russo, 1996) as competitors have individual business objectives and are prone to opportunistic behavior (Quintana-Garcia and Benavides-Velasco, 2004). Moreover, while strategic groups might decide to work together to increase the market for the group collectively, they subsequently compete for the largest share of that market (Gomes-Casseres, 2003). Thus, strategic groups with within group collaboration might end up competing as fiercely as other groups that do not engage in such coopetition relationships. Hence, strategic groups might achieve similar performance outcomes despite their opposing strategies.
In conclusion, strategic groups reflect the patterns of collaboration in the automotive industry to the extent that they reflect production alliances. Thus, competitors pool their resources and capabilities with the aim of achieving economies of scale. Additionally, competitors pool their knowledge, while also acquiring complementary knowledge for innovation purposes. Thus, in the context of manufacturing and innovation strategic groups provide a structural explanation of their conduct.
purpose of managing the continuous pressure of cost reduction in a capital-intensive industry.
6. Limitations
First, databases in general provide limited access to sophisticated data that reflects competitive strategy. Due to the nature of the data firms are more inclined to keep as much data as possible from publication. For instance, competitors might use this data to improve their industry position. Furthermore, due to a high correlation among strategic variables, variables were excluded from the cluster analysis, which were identified important to analyze competitive dynamics within the chosen industry. In addition, the analysis incorporated a small selection of firms, as the selection round of strategic variables excluded firms. The last two points are correlated in the sense that the selected research process and time constraints at the end eliminated the chance to rectify this. With the ultimate variables, more firms might have met the criteria to be included in the cluster analysis. Moreover, although employing performance variables provide a valuable research stream, it is difficult to infer that the specific alliances formed have direct impact on the performance of the firm. In order to infer performance was a direct result of a certain type of alliance, a quantitative analysis such as interviews with automotive managers might be more valuable.
7. Future Research
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