TIMING IN BUSINESS MODEL AND PRODUCT MARKET STRATEGY TRADEOFFS: PERFORMANCE IMPLICATIONS

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1 TIMING IN BUSINESS MODEL AND PRODUCT MARKET STRATEGY TRADEOFFS: PERFORMANCE IMPLICATIONS DZIDZISO SAMUEL KAMURIWO Cass Business School City University London 108 Bunhill Row, EC1Y 8TZ, London ABSTRACT I examine the contingent effects of business model design and product market strategy in Technology-Based Firms and their dual antithetical performance implications. The results show business model and product market strategy design choices, when viewed as compliments and undertaken simultaneously, allow firms to achieve otherwise antithetical performance outcomes. INTRODUCTION Recent studies have begun to extend the much studied question of strategy/structure fit by exploring other structural forms of organisation such as business models and their fit with product market strategies (e.g. Yin and Zajac, 2004; Zott and Amit, 2008). These studies have pointed to the novelty of the business model construct resulting from rapid and extensive technological developments in the past decade that have made firm boundaries more permeable (e.g. Mendelson, 2000) and the heightened role of a well considered product market strategy in potentially increasing the firm s ability to create and capture value (e.g. Zott and Amit, 2008), especially in contexts where market uncertainties in dynamic markets are high. While this pair of variables has received much attention lately, little research has been conducted on the issue of timing of business model and product market strategy design (Zott and Amit, 2008: 20) i.e. whether they are determined simultaneously or whether one follows the other: Does the contingent relationship between the two variables have a timing element to them? This paper addresses that gap by examining how the contingent effects of product market strategy and business model choices address the need for technology based new firms (TBNF) to be (1) swift in bringing products to markets and (2) having a rich product pipeline. These two performance outcomes are both desirable but are antithetical because they each require different design trade-offs and one represents short term benefit (speed) and the other longer term benefits of developing a rich product pipeline (e.g. Puranam, Singh and Zollo, 2003) In this study, the structural element is the organisational architecture (Chesbrough and Rosenbloom, 2002) as described by the relationship between the focal firm and its exchange partners such as customers, vendors and complementors (e.g. Zott and Amit, 2008). The organisational architecture also reflects the rationale of how the managers believe the firm will take its products to market (e.g. Teece, 2007). TBNF structures can be virtual (e.g. Chesbrough and Teece, 1996) i.e. a loosely coupled network that is designed to benefit from speed or flexibility in accessing resources or knowledge, or tightly coupled networks in which focal firms prefer to integrate in-house particular value chain activities and focus on benefiting from efficiency gains of integration (e.g. Grant, 1996).

2 Whilst TBNF can create and capture value through business model design, a product market strategy in dynamic market contexts is potentially valuable because the knowledgeproduct linkages are uncertain (e.g. Grant, 1996) and it may be beneficial for firms to place bets in different markets and create scouting options (McGrath and MacMillan, 2000). The paper develops a set of hypotheses that are tested on a unique dataset of biotechnology firms business models and product market strategies. I establish empirically that the contingent effects of making these structure/strategy choices allows otherwise antithetical performance outcomes to be reconciled. The study attempts to make several contributions to the strategy literature by extending the academic perspective of contingency effects in introducing a timing element; examining new aspects of structure and strategy and providing an empirical test of these issues. THEORY AND HYPOTHESES DEVELOPMENT Contingency Interaction of Structure and Strategy This study employs contingency theory to examine the strategy/structure fit relationship to encompass timing and new measures (business models and product market strategy) to take into account developments in recent decades that has produced new contexts (e.g. Yin and Zajac, 2004; Zott and Amit, 2008; Foss, 2002). The Business Model Architecture of TBNF Rather than characterising business models by their design theme (Zott and Amit, 2008: 4), this study conceives TBNF business models as purely structural constructs that are defined by organizational arrangements of how the firm chooses to connect with factor and product markets (e.g. Chesbrough and Rosenbloom, 2002). In TBNF contexts that have a regime of rapid technological development relevant organisational capabilities are not easily assembled in a single firm (e.g. Powell, Koput and Smith-Doerr, 1996). In these contexts, theories of the firm have not comprehensively been able to explain how these structures work or explain why firms perform certain activities in-house while using external partners for others (e.g. Grant, 1996). Proponents of the knowledge based view of the firm consider it to be a superior context for efficient knowledge integration (e.g. Grant, 1996; Kogut and Zander, 1996) and yet recognise the importance of external partners in providing greater flexibility. TBNF are severely impeded at founding because they have limited capacity to invest and develop complex parallel capabilities simultaneously i.e. internal capabilities for product development AND alliance management capabilities (e.g. Sapienza et al, 2006). Hence making the tradeoff decision is important to TBNF. In this paper, those firms that integrate critical stages of the product development process and use alliances selectively are termed tightly coupled. Firms that prefer to use external partners even for critical stages of the product development process and are really hollow decide to externalise knowledge are termed loosely coupled (Orton and Weick, 1990). On the one hand, loosely coupled structures are virtual business models whose advantage is that it provides flexible access to a set of complex knowledge sources that would take a lot of time and effort for the focal firm to build. On the other hand, tightly coupled structures invest in unique

3 capabilities for long term advantage and have a potentially superior context for superior productivity. Hence the following hypotheses: Hypothesis 1. Loosely coupled structures will outperform tightly coupled structures firms in terms of speeding products in development to market Hypothesis 2. Tightly coupled structures will outperform loosely coupled structures in terms of having more products in development Business Model and Product Market Strategy Fit TBNF are in a dynamic environment where target markets are uncertain or undeveloped and hence whose prospects and outlook can change (e.g. McGrath and MacMillan, 2000). Tightly coupled business models commit early to a technology approach and internalise critical stages in the product development process thus creating commitment which leads to a path dependency which is associated with narrow scope and fixed purpose (e.g. Ghemawat and del Sol, 1998). However, a loosely coupled structured firm has more flexibility than a tightly coupled structured firm because the firm s value chain activities or its components can be either disengaged or recombined in different ways, often to serve different markets (e.g. Schilling and Steensma, 2001; Grant, 1996). Hence I can posit that: Hypothesis 3. Positioning in more customer segments will be more beneficial to a loosely coupled structured business model than to a tightly structured business model in terms of speeding products under development to market and in having more products under development Research Setting, Data and Measures METHODS The biotech sector is a suitable context for this study because first mover advantages and product market strategic flexibility are important as the risk of failure is high. The study employs a large sample of UK based biotechnology firms founded between 1995 and In line with past research (Rothaermel and Deeds, 2004), the firms were identified from relevant official sources and carefully chosen to ensure comparability. Dependent variables products in development and time to market. The dependent variables that denote successful knowledge integration in this sector are (1) number of products in development (a count of each firm s products in development that has successfully entered clinical trials (Rothaermel and Deeds, 2004) (2) speed or time to market (months it took each firm from founding to the first announcement that its first lead compounded or product has entered the clinic). Independent variables organizational structure. I classified the firms according to the amount of internalised knowledge or degree of vertical integration by comparing the firm s investment in capabilities with the traditional drug development value chain stages, the structure of which is well documented (Rothaermel and Deeds, 2004). I denoted as loosely coupled firms as firms that did not invest in laboratory facilities and preferred to outsource their drug discovery and development stages through alliances or the market place. I designated firms as tightly coupled those that invested in a laboratory earlier on at founding and systematically recruit inhouse bench scientists as they internalised critical stages of the product development process.

4 Independent variable number of customer segments targeted. I measured each firm s market scope by the number of therapeutic areas the firm is involved in. Control variables cumulative funds invested and age and VC involvement. These variables were included because they impact successful knowledge integration within firms. The first, cumulative funding, is a proxy for firm size and is the cumulative amount in sterling of funds the firm raised. The second is the age of the firm in months from founding since older firms are more likely to have more experience and hence more products in development. The third variable measures a firm s ability to access venture capital funding. Estimation Procedure The regression methods I used take account of the nature of my chosen dependent variables. The first set of regressions uses an integer valued or count outcome (products in development) and is best modelled by a Poisson regression model. The second set of regressions involves a censored or truncated dependent variable (time to market) and used a right censored normal Tobit quadratic hill climbing model (Scott Long, 1997). RESULTS The descriptive statistics in Table 1 provide results that are comparable to those obtained in other similar biotech sector studies particularly with regards to control variables (e.g. Roethermel and Deeds, 2004) Insert Tables 1, 2 and 3 about here The evaluation of the principal hypotheses 1, 2 and 3 is shown in Tables 2 and 3 below. Model 4 in Table 2 and 3 represents results for the full model when all variables are entered and best fits the data. DISCUSSION AND CONCLUSION In this study, I built on the notion (from past research) that the firm s business model and its product market strategy are distinct constructs that present two separate and different opportunities for creating and capturing value (e.g. Zott and Amit, 2008). I use in this study business model constructs that capture the structural template of the firm, designated as loosely coupled and tightly coupled structures. I found significant interaction with product market strategy as measured by the number of customer segments targeted on the dual performance outcomes of speeding products to market and number of products or richness of product pipeline. Given that the two outcomes represent antithetical objectives for a TBNF, the contingent effects of design choices of business model and product market allow these to be effectively reconciled. A tightly coupled structured business model that would have problems with speeding products to market because capabilities take time to develop, could achieve speed and have a rich product pipeline when it deploys an appropriate product market strategy i.e. focus albeit in blockbuster markets. Similarly loosely coupled structured business models can achieve both outcomes by positioning in many possibly niche markets.

5 This study makes several important contributions. Firstly having been inspired by recently revived interest in the study of the strategy/structure relationship, my aim is to inspire this on-going revival in terms of the structural construct and its implications to the question of the boundaries of the firm. I do so by further establishing the contingent role of the firm s business model and its link to value capture. I show that value is created and captured when the interaction effects are determined simultaneously with the product market strategy of the firm. I build on much recent research that considers the structural element to be transcendent of the administrative boundary, an aspect that is more representative of how firms are creating and capturing value differently in recent decades (e.g. Foss, 2002). Second, in examining the fit between the firm s business model structure and its product market strategy, I used different measures that allowed me to explore another facet of product market strategy linked to TBNF in dynamic markets positioning in customer segments. In contexts where there is high market uncertainty, it is common to attempt to deploy a firm s capabilities in different markets as a hedge against risk (McGrath and MacMillan, 2000). These decisions do not necessarily emerge from the preferred structure of the firm but are a clear separate approach to value creation and capture. The notion of good fit arises from the finding that otherwise antithetical objectives of short and long term objectives can be reconciled by appropriate interaction effects of structure and strategy. Third, by empirically testing the timing aspects of the contingent effects of business model and product market strategy constructs, the study underscores the importance of the constructs as sources of competitive advantage. Different business models can contribute to a firm s competitive advantage independently and jointly as does the product market strategy thus supporting previous research (e.g. Zott and Amit, 2008; Christensen, 2001). However, the need to meet potentially antithetical or paradoxical strategy outcomes mean that they may need to evolve in a certain manner. This study has shed more light on how business models compete effectively within an industry and how firms are positioned differently, a question that some scholars have raised (e.g. Magretta, 2002). REFERENCES AVAILABLE FROM THE AUTHOR Cum. funds inv. m TABLE 1 Descriptive Statistics Tightly Coupled structure No. of Customer segments Venture capital inv. Products in dev # Time to mkt (months) Age (months) Mean Median Std. Dv # of Obs

6 TABLE 2 Regression Results: Dependent Variable - Products in Development Model 1 Model 2 Model 3 Model 4 Constant -1.90*** -1.79** -1.85** -2.47*** Age (months) 0.016* 0.016* 0.017* Cumulative Funds Invested m 0.021** 0.022** 0.02** 0.027*** Venture Capital Involvement Tightly coupled structure ** Number of customer segments ** targeted Tightly coupled structure x Number -1.17*** of customer segments targeted Log Likelihood R square Adjusted R square Number of Observations * p<0.05; ** p<0.01; *** p<0.001; p<0.1 TABLE 3 Regression Results: Dependent Variable Time to Market Model 1 Model 2 Model 3 Model 4 Constant 86.13** 85.16** 89.46** *** Age (months) * Cumulative Funds Invested m -0.98* -0.99* -0.98* -1.05** Venture Capital Involvement Tightly coupled structure * Number of customer segments ** targeted Tightly coupled structure x 38.66*** Number of customer segments targeted Log Likelihood Right Censored Observations Uncensored Observations Number of Observations * p<0.05; ** p<0.01; *** p<0.001; p<0.1

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