The technological acquisitions paradox in the beauty industry

1. Introduction

Our work offers an original perspective of analysis on how technological acquisitions and knowledge recombination shape the innovation trajectories of the acquirer company. It does so by embracing an inductive research design, drawing on a single but very powerful case study, which analyses detailed acquisitions and patent data information of a market leader in the beauty industry, namely L'Oréal. As well put by Chiaroni et al. (2011), innovation research is often based on the observation of innovation strategies of high-tech companies, disregarding the wider portfolio of companies innovating in other industries, largely considered as low-tech. The beauty industry represents a peculiar and paradoxical case that illustrates the complexity of the knowledge recombination process, which deserves scientific attention from the innovation management scholars (Kumar et al., 2006).

Firms' innovation processes have been widely investigated from different perspectives. The current dominant paradigm is the open innovation model (Chesbourgh, 2003), which has come to constitute a new organizational imperative. The model has been implemented in a variety of ways. Some authors have focused on strategic alliances (Mowery et al., 1996; Doz, 1996; Das and Teng, 2000; Dyer et al., 2001; Rosenkopf and Almeida, 2003; Grant and Baden-Fuller, 2004; Phelps, 2010), while others have studied the breadth and depth of collaborations aimed at innovation (Laursen and Salter, 2006). A small number of studies have highlighted the role of mergers and acquisitions (M&As) in supporting the innovation activity of firms (Cloodt et al., 2006; Puranam and Srikanth, 2007; Grimpe and Hussinger, 2014; Sears and Hoetker, 2014; Orsi et al., 2015), still not precisely addressing the direction of their impact. Although the strategic use of acquisitions to acquire new knowledge and capabilities has become a well-institutionalized corporate phenomenon (Vermeulen and Barkema, 2001; Uhlenbruck et al., 2006), empirical evidence of how firms have recombined acquired knowledge remains scarce. However, it has been demonstrated that open innovation is more effective than closed innovation, and increases employees' performance (Alawamleh et al., 2018). Moreover, while several studies have focused on the role of technological relatedness in leveraging absorptive capacity during acquisitions (Ganzaroli et al., 2016), few have extended their interest to unrelated knowledge (Hagardon, 2003; Schoenmakers and Duysters, 2010) to explore the effect of this kind of knowledge acquisition on the specialization- or diversification-led technological orientation of the acquirer. We analyze these phenomena by defining knowledge as similar, complementary or unrelated to existing knowledge of the acquirer, in accordance with the technological classes in the patent portfolios of the acquiring firm and its target firms. By doing so, we add to innovation management literature by offering a detailed analysis, through patent data, of the knowledge recombination process led by technological acquisitions. In particular, we address the following research questions: (1) Is the innovation trajectory of the acquirer affected by previous acquisitions? (2) In which direction knowledge recombination from acquisition is pushed further? (3) Is the technological acquisition more a mean for knowledge exploration and radical innovation or, on the contrary, a way for consolidating previous technological specialization?

In order to answer the research questions, our work identifies various ways of using the target's knowledge and the different forms of recombination that can affect the technological patterns of knowledge accumulation and growth (Antonelli et al., 2010; Gruber et al., 2013). Technological acquisitions enable acquiring companies to access the target's knowledge, which may be related (similar or complementary) or unrelated to the knowledge of the acquirer. After the acquisition, the acquirer's capacity to exploit this external knowledge (acquired knowledge) depends on the ability of the acquirer to recombine new and owned knowledge to realize novel technological combinations, or to refine known combinations for new uses and applications – developed knowledge (Kogut and Zander, 1992; Carnabuci and Operti, 2013). By matching the characteristics of acquired knowledge and developed knowledge, we can identify how a technological acquisition affects the technological trajectory of the acquirer.

Specifically, thanks to the findings coming from the L'Oréal case study analysis, we have identified and contrasted two possible post-acquisition technological trajectories: specialization and diversification. An acquirer that follows a trajectory of specialization seeks to innovate in its core technology class or in classes with similar technological principles. Conversely, an acquirer following a trajectory of diversification seeks to innovate in new technology classes that are far from its existing knowledge domain. The capacity to balance diverse kinds of acquired and developed knowledge might be the result of ambidexterity or a punctuated equilibrium strategy (Gupta et al., 2006), and might affect both the breadth and the depth of the acquirer's technological portfolio. From this perspective, acquired and developed knowledge respectively represent the input and the output of the recombinant process involved in the technological acquisitions (Hitt et al., 1998).

Data were triangulated from a variety of sources, including press releases, journal articles, books and database records on patents and acquisitions. The paper is structured as follows. Section two presents the theoretical background. Section three explains the methodology. Section four presents our major findings, which are further discussed in section five. Finally, section six summarizes our main contribution, the implications for theory and practice, and, as usual, limitations and directions for future research.

2. Theoretical background

The speed of technological innovation and the rapid technology obsolescence in high-tech environments has forced innovative companies to shorten the time they spend on research and development (R&D) and to reduce risks while keeping their competitive advantages (Tsai et al., 2018). To be innovators or fast followers in dynamic and highly competitive technological markets, organizations must balance their internal R&D capabilities with external knowledge (Schildt et al., 2005), which they can acquire through collaborations, strategic alliances, joint ventures and acquisitions (Ahuja and Katila, 2001).

Technological takeovers have specific advantages that do not apply to other acquisition modes, since they enable companies to acquire external and consolidated technological inputs quickly, and focus their internal resources and capabilities on recombining acquired knowledge, instead of developing new knowledge ex novo, thereby hastening innovation performance, and reducing the time to market. Several studies have shown that a firm's recombination capabilities mainly depend on its absorptive capacity (Cohen and Levinthal, 1990). Zahra and George (2002) distinguished between potential and realized absorptive capacity. Whereas the former involves acquisition and assimilation, the latter focuses on transformation and exploitation. Todorova and Durisin (2007) stressed the iterative links between assimilation, transformation and exploitation. More recently, Patterson and Ambrosini (2015) reconceptualized absorptive capacity for research-intensive firms by focusing on the iterative nature of the assimilation process, which must be developed during, rather than solely after, the acquisition process, and should be continually adjusted throughout the transformation and exploitation phases. These contributions indicate that acquisition and assimilation are necessary but not sufficient conditions for innovation, because they must be complemented by recombinant capabilities (Carnabuci and Operti, 2013; Verhoeven et al., 2016; Nagle and Teodoridis, 2020). On this point, Deng (2010) argued that some firms can acquire and assimilate new external knowledge from targets but may fail to transform and exploit it successfully. Similarly, Vasudeva and Anand (2011) and Orsi et al. (2015) focused on knowledge utilization, measuring the extent to which acquirers can use external acquired knowledge to generate innovations.

The importance of existing complementarities between the acquired and the target knowledge bases has been widely emphasized in more recent studies on acquisition processes (Rothaermel and Boeker, 2008; Junni et al., 2015; Makri et al., 2010; Zaheer et al., 2013; Bauer and Matzler, 2014). M&As provide opportunities for firms to complement and renew their knowledge base (Bresman et al., 1999). Thus, knowledge acquisition and knowledge recombination, which involves the exploitation and exploration of internal and external knowledge flows, enable firms to expand their knowledge base by capitalizing on opportunities for both incremental and radical innovation (Chesbrough, 2003; Love and Roper, 2009; Lichtenthaler, 2009). Similarly, Makri et al. (2010) highlighted that technological similarity supports incremental processes of innovation while complementary acquisitions lead to discontinuous strategic transformations.

Access to external knowledge sources, combined with internal R&D, might boost a more creative recombination process (Rothaermel and Alexandre, 2009; Lin and Wu, 2010), conducive to explorative rather than exploitative innovation performance (Lavie et al., 2010; Ganzaroli et al., 2016). From this perspective, multiple technological acquisitions can enable companies to develop an ambidexterity strategy to support exploitative and explorative performance by combining technologically differentiated types of knowledge. A repeated acquisitions strategy provides opportunities for learning (Öberg, 2011) and establishes a routine of exploration, enabling firms to explore “adjacent possible” trajectories (Kauffman, 2000). Such a strategy is conducive to the development of novelties and innovations. The benefits of multiple technological acquisitions depend on a firm's capacity to recombine similar, complementary or even unrelated external knowledge. This can extend the breadth and depth of the firm's technological domains (Katila and Ahuja, 2002). In contrast, this process might involve a trade-off between external technology acquisition and internal R&D expenditure when available resources are limited (Kang et al., 2015).

3. Method

Based on the theory of technological acquisitions and knowledge recombination, our work aims at answering three original research questions: (1) Is the innovation trajectory of the acquirer affected by previous acquisitions? (2) In which direction knowledge recombination from acquisition is pushed further? (3) Is the technological acquisition more a mean for knowledge exploration and radical innovation or, on the contrary, a way for consolidating previous technological specialization?

Because the area of investigation is relatively novel, the nature of this study is exploratory; therefore, an inductive approach is appropriate. We focus on the collection of rich quantitative and qualitative data through case study research (Yin, 2003). We do not test hypotheses statistically, but instead, we aim to offer some useful propositions, which we discuss in light of theory and that can be further tested using a deductive approach (Eisenhardt, 1989). In particular, this research uses a single case study design to provide an in-depth analysis of a real-world case (Yin, 1981, 2014; Eisenhardt, 1989). As suggested by Siggelkow (2007, p. 22), “research involving case data can usually get much closer to theoretical constructs and provide a much more persuasive argument about causal forces than broad empirical research can”. The benefits of a case study approach include the ability to combine a broad knowledge of the context with a higher level of detail, supporting a more in-depth understanding of the innovation process in the context of an acquisition strategy. Evidently, focusing on a single case study limits the generalization of results. However, it inspires some interesting propositions able to positively feed the debate concerning the interplay between technological acquisitions and knowledge recombination process. This case study focuses on L'Oréal [1], a large company operating within the beauty industry. This company was chosen for two main reasons. First, L'Oréal is a particularly innovative company, which invests approximately 3% of its yearly sales revenue in internal R&D (Belussi et al., 2016) and has an impressive patenting record. L'Oréal's portfolio currently includes more than 14,000 patents in 289 four-digit (and 1,372 seven-digit) technological classes (according to our search in the Orbit database). Second, in its history, L'Oréal has carried out many acquisitions, which indicates a strong external growth strategy.

Following Mathison (1988), we aimed to improve the reliability and validity of our research findings through data triangulation. Data were triangulated from different sources. First, qualitative data were retrieved from the L'Oréal website and press releases collected in the 2009–2015 period, as well as journal articles and books on the global cosmetics industry and the insightful work of Jones (2010), in order to explore the L'Oréal's acquisition strategy and to better classify “technological” acquisitions with respect to “strategic” acquisitions. Then, quantitative data were collected from Zephyr – Bureau van Dijk database, which provided information about acquired targets, and Questel Orbit database, which provided data on L'Oréal and acquired targets' patent portfolios.

Preliminarily, we started by mapping L'Oréal's acquisitions from 1928 (the year of the company's first acquisition) to 2015. We retrieved this information from the Zephir – Bureau van Dijk database. We counted 52 acquisitions and classified these as either “technological” or “strategic,” following a widely accepted conceptualization based on two criteria (Ziedonis, 2004; Hung and Tang, 2008; McCarthy and Aalbers, 2016). First, a technological acquisition is defined as involving a target firm that exhibits patenting activity. Where the acquired target continued to operate independently after the acquisition, we also consider its patenting activity after the acquisition. Second, the patents of the acquired firm should be cited in the patent documents filed by the acquiring firm after the acquisition. Patents that are co-assigned to both target and acquirer are excluded, since they are derived from cooperative research activity. Therefore, we analyzed the 14 technological acquisitions in which the target firm had at least one patent cited by L'Oréal after its acquisition. Additionally, we controlled for information collected throughout qualitative data in order to ensure the technological motivation of acquisition.

Then, we followed a three-step empirical strategy, as shown in Figure 1, whereby we first identified the characteristics of what we labeled acquired knowledge (first step). Second, we identified the characteristics of what we labeled developed knowledge (second step). Finally, we assessed the degree to which the acquired knowledge matched the developed knowledge, which informed us about the technological trajectory of the firm after its acquisitions (third step).

Operationally, in the first step, we collected all patents of targets cited by L'Oréal after an acquisition, and then compared the technological classes of the cited patents with those of L'Oréal before the date of acquisition (depending on the target). This enabled us to measure the distance between existing and acquired knowledge according to the technological classes, and in turn to identify the characteristics of the acquired knowledge. Technological classes were defined according to the International Patent Classification (IPC) provided by the World Intellectual Property Organization (WIPO). In so doing, we distinguished between similar, complementary and unrelated knowledge. Acquired knowledge was classified as similar when its technological class was detected in L'Oréal patents portfolio at the seven-digit IPC level. It was considered complementary if it was only detected in the L'Oréal patent portfolio at the four-digit IPC level. Finally, it was considered unrelated if it was completely new to the L'Oréal portfolio of technologies [2].

In the second step, we primarily collected all citing patents (patents filed by L'Oréal that cited targets), in order to compare the technological classes of the citing patents (at the four- and seven-digit IPC level) to the existing technological classes of L'Oréal's portfolio until one year before the priority date. Adopting the same methodology discussed above, we distinguished between similar, complementary and unrelated developed knowledge.

Finally, this enabled us to compare acquired and developed knowledge and to assess (in the third step) whether the L'Oréal's technological trajectory was oriented more toward specialization or diversification.

4. Results

5. Discussion

This article comprises a historical analysis of L'Oréal's innovation strategy in relation to its significant technological acquisitions. The first finding concerns the extent to which this leading global firm, during the last four decades, based its innovation activity on a model of closed innovation. In fact, of its intensive knowledge production, covering (more than) 14,000 EU patents registered by L'Oréal (Orbit database, 1928–2015), only a small portion is from the recombination of external knowledge deriving from takeovers. Only 14 of L'Oréal's 52 acquisitions were related to technology. Thus, external acquisition of knowledge appears to have played a marginal role (only 91 patents of L'Oréal's patents cited targets that the company had acquired) in the company's innovation strategy, with developed knowledge derived from the recombination process accounting for less than 4% of its cumulative inventing activity. The case of L'Oréal presents a challenge to the imperative of open innovation and shows the effectiveness of the old internal model of innovation. The evidence we collected enabled us to trace L'Oréal's technological trajectories, which indicate an internal linear model of knowledge accumulation studded by a small sequence of technological acquisitions of companies that generally owned knowledge that was similar to L'Oréal's existing knowledge.

In particular, our findings confirm that the absorption of similar and complementary knowledge is conducive to an innovation trajectory that strengthens the firm's technological specialization. The acquisition of similar knowledge is less costly for an organization and better supports the transformation of acquired knowledge into exploitable knowledge (Deng, 2010). Firms more easily recognize and absorb external knowledge close to their existing knowledge base (Cohen and Levinthal, 1990), because they can easily spot its potential. Consequently, acquisition strategies are more often directed toward firms operating in close knowledge domains, where the result of knowledge recombination is more predictable, making the acquisition less risky. However, the closeness between the acquirer's and the target's knowledge bases and the technological overlap limits the innovative output (Nooteboom et al., 2007). Similarly, technological proximity reduces the acquirer's opportunities for learning (Hitt et al., 1996), resulting in an incremental rather than a radical innovation process. Thus, it has been claimed that the development of radical innovations might depend more on access to knowledge from different scientific domains and knowledge sources, on the condition that the distance is not too large (Colombo and Rabbiosi, 2014; Cassiman and Veugelers, 2006; Sears and Hoetker, 2014). This aligns with recent studies on regional innovation policies, which have found technological relatedness to be a critical driver of technological development and progress (Boschma, 2017; Apa et al. 2018; Balland et al., 2019). Makri et al. (2010) argued that related knowledge can include not only similar knowledge but also complementary knowledge, both of which can contribute to a specialization path (Ganzaroli et al., 2016). Therefore, Proposition 1 is derived.

When the technological distance is significant, the invention process may be more complex and costly (Nooteboom et al., 2007). Nevertheless, several studies have highlighted that technological unrelatedness can trigger more radical forms of invention (Phene et al., 2006; Frenken et al., 2007; Castaldi et al., 2015). In such cases, external knowledge acquisition can increase a firm's ability to profit from path-breaking (Nagle and Teodoridis, 2020). This finds theoretical justification in the Schumpeter's idea (1911) that radical path-breaking innovations result from novel combinations of knowledge. However, unrelated knowledge combinations remain unexplored as sources for new path development and structural change (Grillittsch et al., 2018; Miller et al., 2007). Often, the process of creating radical breakthrough technologies through acquisition requires novel competences and high coordination abilities, the lack of which can hinder innovation (Grigoriou and Rothaermel, 2017).

Research has shown that non-equity venturing alliances are more likely to be useful for explorative learning than joint ventures or acquisitions (Schildt et al., 2005). Interdivisional knowledge among diverse firms has a stronger impact on the quality of innovation than knowledge that exists within the boundaries of a single firm. This supports the hypothesis that the most diverse types of knowledge trigger the most radical forms of innovation (Miller et al., 2007).

The acquisition of unrelated knowledge is usually costlier and riskier because of the technological distance between the acquirer's and the target's knowledge domains. Low levels of absorptive capacity can further inhibit a firm's ability to transfer and integrate new unrelated knowledge. Conversely, firms with significant experience of M&As and high levels of absorptive capacity can overcome technological distance and more efficiently support the knowledge recombination process, which, nevertheless, leads to sustain pre-existing technological trajectories, obeying to a specialization path. Despite the potential for radical innovation from the acquisition of companies owning unrelated knowledge, the case illustrates that even when the process of exploration of distant knowledge domains is available, the company prefers to use that knowledge to reinforce the technological specialization, creating products and technologies that conformed to its preexisting technological patterns. Hence, we propose the following proposition:

Paradoxically, the case informs that in case of development of unrelated knowledge, this was due to the exploitation of the collaboration with acquired companies specialized in related knowledge domains. This is the most interesting result of the study, which asks for further testing by scholars willing to deepen the “dark side” of open innovation, which, as our case illustrates, is not necessarily conducive to radical innovation.

Overall, our analysis shows that L'Oréal's innovation strategy was strongly path dependent. L'Oréal did not use the open innovation approach to explore new knowledge domains. Rather, it engaged in repeated acquisitions to develop its existing technological trajectory through the realization of incremental innovations that have strengthened its technological specialization. This trajectory aligns with Hargadon's (2003) argument that innovators rarely come up with completely novel ideas; instead, they recombine old ideas into new ones, adapting them from one context to another. This is supported by the fact that in some cases, L'Oréal used similar knowledge acquired from the target to develop unrelated knowledge, which suggests that a high level of specialization is required for innovation in more distant domains. Since its first patent, for a hair dye formula containing a blend of harmless chemical compounds, which was filed on the 24th March 1908 by the company's founder, the French chemist Eugène Schueller, L'Oréal linked the company's growth with the development of the global “market of beauty.” Its strong innovation strategy enabled the company to become the undisputed “queen” of the beauty industry.

Our empirical evidence shows that in large organizations, invention is still the result of a long accumulation process, predominantly based on the recombination of existing knowledge owned by the organization for new uses and applications. As Schoenmakers and Duysters (2010) emphasized, recombining existing knowledge can lead to very important innovations and sometimes to new knowledge related to an entirely new domain. An emblematic case is that of hair dyeing shampoo, which combines competences in shampoo production and hair dye production.

To sum up our evidence and open a discussion on the effects of technological acquisitions on the innovation trajectories of firms, we propose a taxonomy of knowledge recombination through acquisitions, shown in Figure 3.

Four innovation trajectories can be identified: (1) pure specialization, (2) recombinant specialization, (3) related diversification and (4) pure diversification. The first trajectory pertains to companies seeking to exploit their core competences and reinforce their competitive advantage as leaders in the market through the acquisition of companies with similar knowledge. The second is typical of companies that are engaged in innovative collaborations with partners and suppliers and that decide to vertically integrate members of their network to internalize complementary knowledge, thereby strengthening their core competences. The third concerns companies that search for complementary or unrelated knowledge to explore new trajectories in conjunction with a related diversification pattern. The last trajectory relates to companies that search far from their own domain of knowledge for unrelated knowledge and acquire companies that can enable them to explore new trajectories and develop technologies not considered in their past innovation strategies.

6. Conclusions