Firm-level effects of staged investments in innovation:
The moderating role of resource availability
Petra Andries
1Paul Hünermund
2Abstract. Integrating insights on firms’ resource availability with bounded rationality and real options arguments, we propose that resource-abundant and resource-constrained firms reap different effects from a staged approach to innovation. We argue that resource availability triggers overoptimism and managerial discretion, and thereby impedes adequate resource reallocation in staged innovation projects, leading to different effects of staging at the firm level.
An empirical analysis of 2,790 German firms confirms that a staged investment approach leads to a higher number of newly started and abandoned innovation projects in resource-abundant firms than in resource-constrained firms. Supplementary analyses suggest that this is indeed because resource-abundant firms demonstrate more overoptimism and managerial discretion. We discuss implications for the real options literature, as well as managerial implications for innovation investment decisions.
Keywords: innovation; real options; staged investment; resource availability; bounded rationality
JEL classification: O32, M10
1. INTRODUCTION
The field of strategic management studies the reasons for differences in firms’ investment decisions and how these differences affect performance (Rumelt, Schendel and Teece, 1994).
One important investment decision relates to the development and commercialization of new products and services, which are essential for firms to survive in the long run (Schumpeter, 1942). However, investment decisions with respect to innovation are challenging, as not only the technical feasibility but also the market interest for these innovations is uncertain (Hauser, Tellis, and Griffin, 2006; Brown and Eisenhardt, 1997). The literature on the economics and management of innovation has tried to improve our understanding of this phenomenon, by
1 Ghent University, Department of Marketing, Innovation, and Organization. E-mail:
petra.andries@ugent.be
2 Corresponding author. Maastricht University, School of Business and Economics, Department of Organization, Strategy and Entrepreneurship. E-mail: p.hunermund@maastrichtuniversity.nl
identifying several factors that influence firms’ decisions to initiate innovation projects and/or abandon them. At the level of the firm, barriers related to both costs and knowledge have been identified. On the one hand, the perceived economic risks inherent to innovation, the direct costs of innovation projects, as well as the cost and availability of financial resources to fund these innovation projects keep companies from initiating innovation projects and/or successfully completing them (D’Este et al., 2012; Leoncini, 2016; Pellegrino and Savona, 2017). On the other hand, lack of qualified personnel and lack of relevant knowledge of markets and technologies prevent firms from investing in innovation projects (D’Este et al., 2012). Moreover, learning from collaboration and external knowledge sourcing (Leoncini, 2016), from past and current innovation experience (D'Este, Marzucchi, and Rentocchini, 2017) and from past failures in particular (Desai, 2015; Leoncini, 2016; Danneels and Vestal, 2020) have been shown to affect innovation performance and the decision to abandon innovation projects.
If costs and knowledge barriers keep firms from making strategic investments in innovation, a crucial question becomes whether and how firms can learn and improve their knowledge while at the same time limiting or even reducing costs. Building on the real options perspective (Bowman and Hurry, 1993; McGrath, 1999; McGrath and Nerkar, 2004), academics and practitioners have advanced the development and financing of innovation projects in consecutive stages as a promising approach in this respect (Cooper, 2008; Block and MacMillan, 1993). The basic principle of staged investment in innovation is that projects only proceed to subsequent stages if evidence from a previous milestone event signals that the risk of taking the next step is justified (Block and MacMillan, 1993). In practice, this often implies that certain predefined criteria—mostly related to technological feasibility, costs, and market response—need to be met.
Only innovation projects that pass these gates receive follow-up financing. The real options perspective proposes that this staged approach to innovation enables firms to set up experiments with a variety of projects, thereby learning about and potentially developing a first-mover
advantage in a broad range of domains (Kogut and Kulatilaka, 1994; Brown and Eisenhardt, 1997). Moreover, staging supposedly allows firms to abandon the least promising of these projects at a relatively low cost and reinvest the freed-up resources in other, more promising ones (Majd and Myers, 1990). As such, the approach appears to combine the best of two main approaches to decision-making under uncertainty that have been advanced in the literature (Alvarez and Barney, 2005, Reymen et al., 2015), namely planning-based approaches that seek to predict and contain uncertainty, and action-based approaches such as improvisation (e.g., Baker, Miner, and Eesley, 2003), bricolage (Baker and Nelson, 2005), and effectuation (Sarasvathy, 2001) that seek to leverage unforeseen events.
However, several authors have identified difficulties firms have with applying the real options framework in practice. Existing models in the financial economics literature involve complex mathematical formulations (Ragozzino, Reuer, and Trigeorgis, 2016) and make strong assumptions about the rationality of decision makers (Miller and Arikan, 2004).3 Moreover, estimating potential returns and costs may be particularly challenging for innovation projects, as historical information may be irrelevant (Ragozzino et al., 2016). In reality, managers have to rely on subjective estimates (Ragozzino et al., 2016) and are likely to apply heuristic decision rules (Bowman and Hurry, 1993; Miller and Shapira, 2004). As such, the mere decision to stage investments does not necessarily imply that the potential benefits of a real options approach will be realized. Cooper (2008) notes the problem that, although there might be a staged approach to investment in place, gates can “lack teeth” (p. 218) and projects may hardly ever be abandoned.
According to Adner and Levinthal (2004), such an “escalation of commitment” happens frequently because the outcomes of innovation projects entail substantial ambiguity. In the 3 Denison (2009) tests the hypothesis that real options thinking, applied as a capital budgeting technique, leads to more adequate project abandonment decisions compared to net present value analysis in an experimental setting. After having received instructions on how to correctly compute the option value of a fictitious project, her test persons were, on average, more likely to abandon underperforming projects compared to the control group. Interestingly, however, 9.76% of participants (MBA and Master of Accounting students) were still incapable of correctly applying the technique despite detailed instructions and an example problem that walked them through the calculations.
absence of unequivocal proofs of failure, managers are prone to fall into “reinforcement traps”, by continuing to invest time and resources in projects with objectively negative performance signals. Without adequate abandonment of underperforming projects and subsequent reallocation of resources into new innovation projects, firms’ ability to explore and learn about a wide range of opportunities is likewise called into question (Klingebiel and Adner, 2015). Overall, boundedly rational decision-making can therefore seriously undermine the practical applicability of the real options approach (Ragozzino et al., 2016).
But even though the real options literature discusses the limitations of staged investment in the light of bounded rationality, few systematic approaches have addressed under which conditions firms that stage their investments are likely to depart from the rational real options logic and how this then affects their investment decisions. As such, we still lack a detailed understanding of the merits of real options reasoning. Ragozzino et al. (2016) argue that “for real options theory to connect with the core of strategic management theory and become a pillar theory in this field, it ultimately must focus on issues of firm heterogeneity” (p. 431). In this respect, the present study focusses on firms’ heterogeneity in terms of resource availability. In particular, it proposes that resource-abundant firms benefit less from staging their innovation projects – than resource-constrained firms. We argue that high resource availability engenders overoptimism and managerial discretion, which in turn create leeway for inadequate decisions on the initiation and abandonment of projects. We advocate that firms with resource constraints, by contrast, are less likely to hold overoptimistic beliefs about their courses of action and are more likely to closely monitor their innovation activities. As a consequence, we hypothesize that in resource-abundant firms, the use of staging will have a lower impact on the number of new projects that are initiated than in resource-constrained firms. At the same time, the use of a staged approach to innovation will influence the number of unpromising projects that are abandoned to a lesser extent in resource-abundant than in resource-constrained firms.
We test these hypotheses for a sample of 2,790 German manufacturing and service firms by combining credit rating information – providing us with a reliable indicator of resource availability – with data from two waves of the German section of the Community Innovation Survey (CIS), which is an official survey by the European Commission launched across Europe.
In addition to the standard catalog of CIS questions, these two German editions include questions on firms’ approaches to investments in innovation – in particular, on whether projects are financed in stages – and on project initiation and abandonment decisions. Results are consistent with our theoretical predictions that the staging of innovation projects has a lower effect on the number of newly initiated and abandoned projects in resource-abundant firms than in their resource-constrained counterparts. Supplementary analyses using other editions of the German CIS suggest that this indeed follows from the fact that resource-abundant firms display more overoptimism and the managerial discretion that makes it possible for this overoptimism to affect innovation investment behavior.
This study adds to the literature on the economics and management of innovation by advancing the staging of a firm’s investment decisions as a way to overcome cost and knowledge barriers to innovation. Moreover, by investigating how firm heterogeneity, and differences in resource availability in particular, moderate the effect of staging on firms’ investment decisions, this study offers a contingency view on real options reasoning and relaxes the assumption of full rationality that tends to dominate the real options literature. Finally, it has important implications for practitioners, as it shows that the use of a staged investment approach to innovation is more likely to improve project decisions in resource-constrained than in resource-abundant settings.
In the remainder of the paper we first present the advantages of using a staged investment approach to innovation from the perspective of a fully rational investor. We argue that staging should allow the firm to initiate a larger number of new projects and to abandon a larger number of unsuccessful ones. We then build on bounded rationality insights to develop testable
hypotheses regarding the moderating role of a firm’s resource availability for these relationships.
We proceed with our empirical approach and findings, and conclude by discussing the implications of our study for theory and practice.
2. THEORETICAL BACKGROUND
This section presents a real options perspective on staging. First, we argue that fully rational firms that stage (some of) their innovation projects, will initiate and abandon a higher number of innovation projects than firms that commit all investments upfront. By incorporating insights on resource availability and bounded rationality, we then propose that these effects of staging will be less pronounced for resource-abundant than for resource-constrained firms.
2.1 A real options perspective on the staging of innovation projects
Both academics and practitioners have been searching for ways to handle the uncertainty inherent in innovation. In this respect, the organization and financing of innovation projects in different stages has been widely proposed as the preeminent way of managing and even benefiting from uncertain opportunities, while keeping costs under control. Such a staging of innovation projects involves “a conceptual and operational map for moving new product projects from idea to launch and beyond” (Cooper, 2008, p. 214). It comprises a series of stages where at the end of each stage a decision is made to either terminate or continue the project. The theoretical underpinning for staged investment can be found in real options theory, which is concerned with investments under uncertainty and (partial) irreversibility (Dixit and Pindyck, 1994). The resulting optimal investment pattern possesses a structure similar to that of financial options (Bowman and Hurry, 1993). A financial option confers on its holder the right, but not the obligation, to sell or buy a specified quantity of an underlying asset at a specified price at or before a specified date. Similarly, a real option gives the firm the right, but not the obligation, to
take some action in the future (Li, James, Madhavan, and Mahoney, 2007). Hence, instead of having to decide on a large upfront investment, the investor can spread his or her investment decisions over time (Cooper, 2008).
This is interesting, as very often, a significant proportion of a project’s inherent uncertainty can be reduced through a relatively small initial investment (see Chi and McGuire, 1996, on uncertainty reduction in collaborative ventures). This “value in learning”4 is particularly relevant in the specific context of innovation, where small initial investments, such as low-cost experiments, allow firms to acquire information on technological feasibility and market acceptance and develop first mover advantages (Brown and Eisenhardt, 1997; Ries, 2011).
Moreover, staging also makes it possible to minimize the risk inherent to such learning, because staging firms hold the option to abandon underperforming projects at relatively lower costs. This is commonly referred to as “the value in abandoning” (Majd and Myers, 1990). As Li et al.
(2007, p. 37) explain: “Given that abandonment before completion saves a portion of the total investment cost, the expected cost to be incurred with some stages still remaining must necessarily be lower than the total investment cost if there exists a positive possibility for the project to be abandoned before completion.” The increased flexibility that firms acquire through real options reasoning stems from the irreversibility of investments (Dixit & Pindyck, 1994;
Rivoli and Salorio, 1996; Li et al., 2007). While firms that do not stage their investment will lose the project’s full sunk costs upon its abandonment, firms that stage their investment will only lose a fraction of that cost.
These opportunities to learn about and abandon any given project at a lower cost, imply that staging will impact the number of projects a firm will initiate and abandon. A firm can look at the existing innovation projects in its portfolio, and decide a posteriori, whether each of these projects should be continued or abandoned. Firms that stage their investments will have incurred 4 The “value in learning” is a standard term in the real options literature, which refers to uncertainty about the value of a project resolving over time. It does not imply learning from failed projects for future R&D activities.
only part of the project’s total sunk costs at any intermediary milestone. Compared to firms that do not stage their investment and have incurred the full sunk costs, they will therefore be more likely to abandon the project (Majd and Myers, 1990; see also Belderbos and Zou, 2009, on real options and internationalization strategies). As a result, it can be expected that a fully rational firm, for any given portfolio of innovation projects, will abandon a larger number of these projects if it stages (at least some of) its investments as opposed to if it does not stage any of them. Moreover, this difference will be more pronounced the worse the signal a firm receives about project value during the runtime of the project (i.e., the lower the expected revenues), and the higher the sunk costs than can be avoided by abandoning innovation projects early.5
When deciding whether or not to initiate a new innovation project on the other hand, a firm will assess its expected value a priori. Since staging allows firms to abandon projects with negative value signals during runtime, it leads to lower expected project costs than if the entire (sunk) investment amount is incurred upfront. This cost difference will be larger if a larger portion of the total costs can be avoided by abandoning the project in an early stage and if the probability of failure is larger (i.e., if the project is more risky; cfr. our mathematical derivation in Appendix). As a result, firms that use a staged investment approach will make a positive upfront assessment for a larger number of innovation projects than if they would not stage their investments. Moreover, given that firms which stage their projects can explore new opportunities with smaller initial investments, they are also able to spread their resources over this larger number of new and positively assessed projects (McGrath and Nerkar, 2004). As a result, the real options literature typically assumes that firms that use a staged approach – even if only for a subset of their investment projects – will initiate a larger number of new projects than firms that do not.
5 Since the real options literature often makes use of formal economic models, we develop a mathematical real options model for staged innovation projects in the appendix, in which we demonstrate in detail the mechanisms that lead to the value of learning and abandoning.
In the remainder of this paper, we will however argue that these general predictions are altered significantly once a firm’s resource availability is taken into account. In particular, we will advance resource availability as an important moderator for the relationship between a firm’s use of project staging on the one hand, and the number of innovation projects it initiates and abandons on the other.
2.2 Resource availability, decision-making biases, and the effects of staging
When describing the expected effects of project staging, the previous section assumed fully rational decision-making; while in reality, investment decisions are subject to decision-making biases. We argue below that these biases are more pronounced in resource-abundant than in resource-constrained firms. As such, we expect a firm’s resource availability to moderate the relationship between staging and the number of newly initiated and abandoned innovation projects.
Decision-making biases and implications for project decisions. It is long known that managers are likely to negate or misinterpret even the most objective information regarding their firms' strategies and environments because of judgmental biases and wishful thinking (Powell, Lovallo and Caringal, 2006, Bazerman, 1997; Kahneman & Tversky, 2000); and that ambiguity – which is likely to occur in innovation trajectories – amplifies these biases (Dunning, Meyerowitz, &
Holzberg, 1989; Van Yperen, 1992). Individuals tend to bias facts to bring them in line with previously held preferences and beliefs (Nisbett and Ross, 1980). Moreover, if something goes wrong, they are much more likely to attribute this to external than to internal causes (Ross &
Staw, 1993). And finally, in line with the latter observation, they systematically overestimate their own competencies – a phenomenon which is called ”self-enhancement” – especially if they believe their assessments cannot or will not be objectively verified (Powell et al., 2006).
These biases are known to affect project decisions in two important ways. First, biasing facts
to bring them in line with previously held beliefs, attributing negative outcomes to external factors, and self-enhancement jointly prompt overoptimism about the duration and viability of projects, something researchers have called the ”planning fallacy” (see George, 2005, for a discussion). Managers then underestimate the time and effort needed to complete a project, and overestimate its potential returns, which generally leads to overinvestment (Durand, 2003).
Second, even when confronted with negative outcomes, overoptimistic managers tend to assume that things will eventually improve if they invest additional time and effort, a phenomenon called the “reinforcement trap” (Ross and Staw, 1993). This typically leads to escalation of commitment for unpromising projects that should have been abandoned instead (George, 2005).
Although these biases are commonly observed at the level of the individual and are known to affect individual decision-making, they are partly determined by the organization to which an individual belongs (McGrath and Nerkar, 2004; Sutcliffe and Huber, 1998; Wood and Bandura, 1989; Hayward and Hambrick, 1997; Durand, 2003). Whereas the literature has discussed this organizational impact rather generally, we believe that in particular a firm’s resource availability may be an important determinant of managerial decision-making biases.
Resource availability and decision-making biases. It can be argued that the amount of resources that is available within a firm will critically affect the decision-making biases of its managers. In particular, we will argue below that managers of resource-abundant and resource- constrained firms will differ with respect to their previously held preferences and beliefs, their likelihood to attribute failure to external versus internal causes, and their tendency to overestimate their own competencies. We propose that, as a result of these differences in biases, managers of resource-abundant will be more (over)optimistic than those of resource-constrained firms, and will therefore take different innovation project decisions.
When looking at resource-abundant firms, it is important to note that managers tend to view available resources as the result of past success (Dasi et al., 2015) and that they model their
beliefs about the environment based on such past successes (Prahalad and Bettis, 1986). As such, we expect that managers of resource-abundant firms will typically hold very positive beliefs about their environment. This implies that they will be likely to bias information on new and ongoing innovation projects to bring it in line with this positive perception (cfr. Nisbett and Ross, 1980). If, for example, initial customer tests point to major technical shortcomings, managers may interpret this as an indication that the customer is interested in an improved version of the product or service. Second, we expect that managers of resource-abundant firms are much more likely to systematically overestimate their own competencies and, in case something goes wrong, to attribute this to external rather than internal causes. In fact, it is known that resource availability relaxes internal control mechanisms (George, 2005). When plenty of resources are available, firms are typically less strict in controlling how these resources are allocated. As such, resource availability allows managers to pursue their own agendas (Cyert &
March, 1963; Jensen and Meckling, 1976; Simsek et al., 2007). It is then unlikely that decision makers’ (biased) assessments will be verified by other actors in the organization, making it much more probable that managers engage in self-enhancement and attribute failures to external rather than internal causes (Powell et al., 2006). As a result of these biases, we expect that managers of resource-abundant firms will be overoptimistic about the duration and viability of their projects (‘the planning fallacy’), and will be more likely to believe that investing additional money will turn unsuccessful projects around (‘the reinforcement trap’).
As for resource-constrained firms on the other hand, we expect that they will be far less likely to develop planning fallacies and fall into the reinforcement trap. First, as their managers may tend to view their firm’s resource constraints as the result of a lack of past success (Dasi et al., 2015), they can be presumed to have either a more realistic or a more negatively biased view of their environment than the managers of resource-abundant firms (cfr. Prahalad and Bettis, 1986).
This in turn implies that they will be either less likely to bias incoming information on new and
ongoing innovation projects, or that they will bring this information in line with their negative beliefs about their environment (cfr. Nisbett and Ross, 1980). If, for example, initial customer tests point to small technical shortcomings, managers of resource-constrained firms may infer from this either that additional technical developments are necessary, or that the customer is not all interested in the product or service. In any case, they will be very unlikely to interpret this customer feedback as an indication of future project success. Second, resource-constrained firms tend to introduce more rigid internal control systems and checks (Cyert & March, 1963). These systems cannot only reduce biases in decision making (Cyert & March, 1963; Jensen and Meckling, 1976; Simsek et al., 2007), but are crucial for drawing relevant lessons from failure (Danneels and Vestal, 2020). We therefore expect that managers in firms with higher internal controls and lower managerial discretion will be less likely to systematically overestimate their own competencies and, in case something goes wrong, to attribute this to external rather than internal causes (cfr. Powell et al., 2006). As a result of these differences in biases, we expect that managers of resource-constrained firms, compared to those of resource-abundant firms, will be far less optimistic about the duration and viability of their projects and about the possibility to turn unsuccessful projects around through additional investments.
We believe that these differences in resource availability and consequent overoptimism will also moderate the effects that firms derive from using a staged investment approach for their innovation projects. As we explained in section 2.1., firms that use a staged approach for (at least some of) their innovation projects are expected to a posteriori abandon a larger number of innovation projects than firms that do not use a staged investment approach. This difference will be more pronounced the lower the revenues a firm expects to generate during the remainder of the project, and the higher the sunk costs than can be avoided by abandoning innovation projects early. However, if at a given stage of the innovation project, a firm believes (a) that the revenues that can be generated in the remainder of the project will be very high, and/or (b) that the
additional cost of further completing the project will be small or can be fully recovered, i.e., if it believes that the additional sunk costs of finalizing the project are minimal, then abandoning the project does not offer much value. Even though the firm stages its investment process, it will be unlikely to discontinue the project (in the appendix we show the occurrence of this effect in our mathematical real options model). More generally, if we look at a firm that stages its investments in innovation, we must conclude that the higher the additional revenues the firm expects to generate by continuing an average project, and the lower the proportion of sunk costs it expects to avoid by abandoning it, the fewer projects the firm will abandon overall. This has important implications when comparing the project abandonment decisions of resource-abundant and resource-constrained firms. As explained above, managers of resource-abundant firms are likely to underestimate the investment and time needed to further complete a project. This implies that, for an average staged innovation project, these managers will estimate the cost reduction from abandoning to be relatively low. Moreover, given their over-optimism, managers of resource- abundant firms are also likely to overestimate the revenues that can be generated by completing the project. As a result, the staging of innovation investments will not have a big impact on the number of innovation projects that resource-abundant firms abandon. By contrast, we expect managers of resource-constrained firms to have more realistic perceptions, and therefore estimate the cost reduction from abandoning to be higher and the revenues from continuing to be lower (as compared to the estimates by managers of resource-abundant firms). As such, we expect that the staging of innovation investments will have a substantial impact on the number of innovation projects that resource-constrained firms abandon. Overall, when comparing those resource-abundant and resource-constrained firms that use a staged investment approach for their innovation projects, we can therefore expect that the resource-abundant firms will abandon fewer projects than their resource-constrained counterparts. Or in other words, we hypothesize that:
Hypothesis 1: The effect of a firm’s use of staging on the number of innovation projects it
abandons varies with that firm’s resource availability. In particular, the effect is smaller in resource-abundant firms than in resource-constrained firms.
Under conditions of rationality, firms that stage their innovation projects are also expected to initiate a larger number of innovation projects than firms that do not stage their projects, because staging raises the a priori expected profit from an average project, and allows to spread a fixed amount of resources over a larger number of these promising projects. As explained above, the increase in expected profit will be smaller if the expected investments needed in later project stages are judged to be lower, since in this case also the cost savings from timely abandonment will be perceived to be less substantial. Furthermore, higher expectations about the cash flow generated by follow-up investment will likewise decrease the difference in expected profits between staged and non-staged projects, because the value of exercising the abandonment option becomes less relevant if the firm believes the project will turn out to be successful (cfr.
appendix). This has important implications when we compare the decision of resource-abundant and resource-constrained firms to initiate new projects. As we argued above, compared to resource-constrained firms, resource-abundant firms typically underestimate the investment and time needed to complete a project, and overestimate the potential outcomes of a project. This implies that staging raises the expected profit from a project to a lower extent for resource- abundant firms than for resource-constrained firms. Given their managers’ overoptimistic assessments, resource-abundant firms that stage their investments in innovation will have very similar profit expectations as resource-abundant firms that commit all investments upfront. As a result, staging will not have a big difference on these firms’ decision to initiate a new innovation project. Resource-constrained firms, on the other hand, have more realistic assumptions, which implies that staging raises their expected profit substantially. These improved profit expectations in turn will positively affect the decision to start new innovation projects. So, although in general, we expect that a firm which stages (at least some of) its innovation projects will be more
likely to initiate new innovation projects than a firm that commits all investments upfront, we believe this effect will be different for resource-abundant and resource-constrained firms. In particular, we can hypothesize that:
Hypothesis 2: The effect of a firm’s use of staging on the number of new innovation projects it
initiates varies with that firm’s resource availability. In particular, the effect is smaller in resource-abundant firms than in resource-constrained firms.
3. DATA AND METHODS
The Mannheim Innovation Panel (MIP) is an annually conducted innovation survey of German firms and part of the pan-European Community Innovation Survey (CIS). Each year, a number of questions not included in the standard CIS instrument are added for purposes of academic research, such as the items used in this article. The MIP is a stratified (according to sector, size class, and region) random sample that complies with the guidelines and definitions of the Oslo Manual (OECD, 2005) for surveys on innovation activities. The MIP covers all production sectors and a large number of service sectors including business-related services, architecture and engineering, and research and development . The use of CIS data has a long tradition in economics of innovation (Cassiman and Veugelers, 2002; Crépon, Duguet, and Mairesse, 1998;
Czarnitzki and Hottenrott, 2011; Czarnitzki and Toole, 2011). Recent contributions show an increased attention also by management scholars (Laursen and Salter, 2006; Leiponen and Helfat, 2010; Leiponen and Helfat, 2011; Klingebiel and Rammer 2014; Klingebiel and Adner, 2015).
Items relevant to our research hypotheses are contained in the 2009 and 2011 waves of the MIP. The gross samples include 35,195 (2009) and 35,531 (2011) firms; net response rates (adjusted for neutral losses) amount to 22.3 percent (2009) and 23.6 percent (2011), leading to
7,061 (2009) and 6,851 (2011) responses, respectively. Compared to other national contributions to the CIS, response rates are lower in Germany, which can be explained by the fact that survey participation is not mandatory – unlike in other European countries – while the questionnaire is significantly longer and more complex. For this reason, the survey is accompanied by an extensive non-response analysis using secondary data as well as primary data collected via telephone interviews with non-respondents (Peters and Rammer, 2013). Due to this extensive non-response analysis, the survey instrument’s annual nature and extensive scope – with items that go beyond the standard catalog of CIS questions – and a careful quality control, the MIP data is generally considered to be of high quality (Klingebiel and Rammer, 2014).
We drop firms that do not report any innovation projects (including the attempt to innovate) from the full sample since our analysis is concerned with how firms that have already decided to engage in innovation activities organize this process. This leads to an intermediate sample size of 5,521 observations, i.e., 2,936 observations from the 2009 wave and 2,585 observations from the 2011 wave. Removing observations with missing values results in a final sample of 2,790 observations, i.e., 1,537 observations from the 2009 wave and 1,253 observations from the 2011 wave. These 2,790 observations represent 2,473 firms in total, as only 317 firms appear in both the 2009 and 2011 wave.
3.1 Variables
The MIP contains survey items on the total number of innovation projects a firm has initiated (New Projects) in a period of three years before the survey year (between 2006 until 2008 for the 2009 wave, and 2008 until 2010 for the 2011 wave). The definition of innovation projects, as used in the Oslo manual and in the survey guidelines, includes but is not limited to pure R&D projects (OECD, 2005, Section 2.5). It covers activities such as new product design, marketing, prototyping, or employee training, which in some industries (e.g. service industries) may be
more relevant than traditional R&D projects. The survey further asks about the number of innovation projects a firm has abandoned in the same period of three years (Abandoned Projects), which is the second dependent variable in our analysis.
Table 1: Descriptive Statistics
Full Sample Split Sample
Mean Std. Dev. Min. Max. Mean
(non-staging) Mean
(staging) Difference (p-val.)
Abandoned Projects 1.37 4.07 0 30 1.40 1.35 0.765
New Projects 8.38 18.70 0 130 9.63 7.66 0.008
Staging 0.64 0.48 0 1 0.00 1.00 -
Resource Availability 377.3 44.29 100 500 379.6 376.0
Firm Size (log) 4.23 1.65 0 12.13 4.37 4.15 0.001
Firm Growth 0.12 0.54 -0.95 10.14 0.10 0.13 0.179
Innovation Intensity 0.01 0.02 0 0.10 0.01 0.01 0.116
Labor Productivity 0.22 0.54 0 16.34 0.23 0.21 0.251
Age (log) 3.05 0.90 0 6.23 34.37 31.22 0.028
Share of Graduates 0.26 0.26 0 1 0.24 0.27 0.017
R&D Cooperation 0.44 0.50 0 1 0.38 0.48 0.000
National Group 0.32 0.47 0 1 0.35 0.31 0.04
Foreign Group 0.10 0.30 0 1 0.10 0.10 0.93
Uncertainty Competitors 1.51 0.76 0 3 1.46 1.54 0.001
Uncertainty Entry 1.29 0.80 0 3 1.29 1.30 0.672
Uncertainty Obsolescence 1.04 0.82 0 3 1.00 1.06 0.079
Year 2010 0.45 0.50 0 1 0.50 0.42 0.000
Notes: N = 2790. Last column depicts p-values for a two-sided t-test on mean differences (equal variances assumed).
Table 2: Correlations
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17)
(1) Abandoned Projects 1.00
(2) New Projects 0.59 1.00
(0.00)
(3) Staging -0.01 -0.05 1.00
(0.77) (0.01)
(4) Resource Availability 0.15 0.16 -0.04 1.00 (0.00) (0.00) (0.04)
(5) Firm Size (log) 0.29 0.36 -0.07 0.44 1.00
(0.00) (0.00) (0.00) (0.00)
(6) Firm Growth 0.00 0.01 0.03 -0.08 -0.05 1.00
(0.97) (0.72) (0.18) (0.00) (0.02)
(7) Innovation Intensity 0.07 0.12 0.03 -0.09 -0.12 0.14 1.00
(0.00) (0.00) (0.12) (0.00) (0.00) (0.00)
(8) Labor Productivity 0.06 0.09 -0.02 0.13 0.09 -0.01 0.02 1.00
(0.00) (0.00) (0.25) (0.00) (0.00) (0.64) (0.36)
(9) Age 0.10 0.08 -0.04 0.36 0.27 -0.20 -0.17 0.07 1.00
(0.00) (0.00) (0.02) (0.00) (0.00) (0.00) (0.00) (0.00)
(10) Share of Graduates -0.03 -0.01 0.05 -0.17 -0.29 0.10 0.30 -0.04 -0.29 1.00
(0.12) (0.59) (0.02) (0.00) (0.00) (0.00) (0.00) (0.02) (0.00)
(11) Cooperation 0.13 0.14 0.10 0.02 0.10 0.04 0.20 -0.02 -0.10 0.24 1.00
(0.00) (0.00) (0.00) (0.38) (0.00) (0.06) (0.00) (0.24) (0.00) (0.00)
(12) Domestic Group 0.14 0.17 -0.04 0.19 0.41 0.01 -0.03 0.08 0.05 -0.10 0.04 1.00
(0.00) (0.00) (0.04) (0.00) (0.00) (0.69) (0.16) (0.00) (0.01) (0.00) (0.03)
(13) Foreign Group 0.04 0.06 0.00 0.02 0.20 -0.04 0.06 0.06 -0.02 -0.01 0.05 -0.23 1.00
(0.02) (0.00) (0.93) (0.28) (0.00) (0.05) (0.00) (0.00) (0.31) (0.51) (0.02) (0.00)
(14) Unc. Competitors -0.02 -0.05 0.05 0.01 -0.03 -0.03 -0.04 -0.01 0.01 -0.07 -0.01 -0.05 -0.01 1.00
(0.34) (0.01) (0.01) (0.55) (0.13) (0.09) (0.05) (0.66) (0.45) (0.00) (0.61) (0.01) (0.51)
(15) Unc. Entry -0.01 -0.04 0.01 -0.02 -0.03 -0.01 -0.05 -0.02 0.01 -0.04 0.00 -0.02 -0.03 0.30 1.00
(0.49) (0.02) (0.67) (0.31) (0.11) (0.44) (0.01) (0.42) (0.69) (0.04) (0.94) (0.33) (0.08) (0.00)
(16) Unc. Obsolescence 0.05 0.05 0.03 -0.06 -0.06 0.03 0.11 -0.07 -0.07 0.19 0.07 -0.03 -0.03 0.11 0.16 1.00
(0.02) (0.01) (0.08) (0.00) (0.00) (0.14) (0.00) (0.00) (0.00) (0.00) (0.00) (0.16) (0.14) (0.00) (0.00)
(17) Year 2010 0.00 -0.03 -0.08 0.00 0.00 -0.07 0.01 0.01 0.02 0.07 0.10 -0.01 -0.01 -0.04 0.05 0.05 1.00
(0.94) (0.17) (0.00) (0.88) (0.97) (0.00) (0.52) (0.76) (0.39) (0.00) (0.00) (0.60) (0.51) (0.02) (0.01) (0.01)
Notes: P-values in parentheses
Table 3: Industry dummies
Industries NACE (Rev. 2.0) Observations Percentage
Food/Beverages/Tobacco 10, 11, 12 95 3.41
Textiles/Clothing 13, 14, 15 88 3.15
Wood/Paper 16, 17 74 2.65
Chemicals/Pharmaceuticals 20, 21 158 5.66
Rubber/Plastics 22 91 3.26
Glass/Ceramics/Concrete 23 69 2.47
Metals 24, 25 217 7.78
Electronics/Electrical 26, 27 362 12.97
Machinery/Equipment 28, 33 336 12.04
Vehicles 29, 30 98 3.51
Furniture/Other Manufacturing 31, 32 93 3.33
Energy/Mining/Oil 5, 6, 7, 8, 9, 19, 35 55 1.97
Water Supply/Waste/Recycling 36, 37, 38, 39 85 3.05
Wholesale Trade 46 51 1.83
Transportation/Postal Services 49, 50, 51, 52, 53, 79 98 3.51
Printing/Publishing/Media 18, 58, 59, 60 116 4.16
IT-Services/Telecommunications 61, 62, 63 197 7.06
Financial Intermediation 64, 65, 66 76 2.72
Consulting/Advertising 69, 70, 73 96 3.44
Technical Engineering/R&D 71, 72 231 8.28
Other Producer Services 74, 78, 80, 81, 82 68 2.44
Other 36 1.29
Notes: NACE stands for “Nomenclature statistique des activités économiques dans la Communauté européenne”. More information: http://ec.europa.eu/eurostat/statistics-
explained/index.php/Glossary:Statistical_classification_of_economic_activities_in_the_
European_Community_(NACE)
Table 1 reports descriptive statistics and Table 2 correlations between variables in our data set. In order to reduce potential outlier effects, we winsorize the dependent variables from above at the 99th percentile. On average, the firms in our sample initiated 8.4 new innovation projects and abandoned 1.4 innovation projects in the previous three years. Note that abandoned projects are not necessarily part of the set of newly initiated innovation projects, as they may have been
started prior to the period documented in the specific survey wave.
Our main variable of interest (Staging) reports whether firms allocate the majority of funds for their innovation projects at the beginning of a project or in stages. We conceptualize this information as a dummy variable that takes on the value one if a firm finances its innovation projects in stages, and zero otherwise. The wording of the respective survey question specifically mentions the possibility that stages could be organized along milestones. Such a staging of innovation projects is reported by about 64 percent of the firms in our sample.6 Table 1 reports split sample descriptive statistics for the group of staging and non-staging firms separately.
As self-reported measures of resource availability using Likert survey scales (see, for example, Keupp and Gassman, 2013) are subject to biases, we prefer to use more objective, secondary data. But whereas several studies have measured resource availability using information on, for example, firms’ return on sales and net profit from annual accounts (see Julian and Ofori-Dankwa, 2013, for an overview), this information is typically missing for small and medium-sized firms, which make up around 80 percent of the observations in our sample (in line with the characteristics of the overall population of German firms). In order to obtain an objective indicator of a firm’s resource availability (Resource Availability) for the different firms in our sample, we thus take advantage of a standardized credit rating index (CRI) for German firms that is produced by Creditreform, the largest credit rating agency in Germany (see Czarnitzki and Hottenrott, 2011, for details on the underlying methodology).7 Creditreform’s ratings are based on financial and economic indicators (total assets, growth, sales, current order situation, etc), past reliability of repayment, general industry risk as well as an individual assessment by Creditreform's local staff about a company’s credit default risk. Since this 6 Staging firms are additionally asked how many steps such a process usually involves. The average lies slightly below four with six being the maximum. However, due to a particularly low response rate and concerns about possible measurement error for this specific item, we rely on the binary indicator in the subsequent analysis.
7 As a credit rating agency, Creditreform’s business model is comparable to that of Dun & Bradstreet in the U.S., for example.
information is collected in order to be provided to banks and other market participants, the ratings thus reflect both the internal as well as external financing situation of a firm. Based on the German school grade system, CRI ranges from 100 (excellent rating) to 600 (worst rating).
We drop firms with a rating higher than 500 from the analysis since this case implies severe financial trouble and close proximity to bankruptcy. In order to facilitate interpretation, we then rescale the index such that: Resource Availability=600−CRI . Our final measure of Resource Availability thus ranges from 100 (lowest) to 500 (highest). In addition, to reduce problems of simultaneity, we lag the variable by one year. Following this procedure, we are able to obtain credit rating information for 97.6 percent of the firms in our survey dataset.
To control for confounding influences, we include other firm-level characteristics in our empirical specification. Innovation activities are likely to be influenced by size, experience, and growth effects (Coad et al., 2016; Aerts and Schmidt, 2008; Veugelers and Cassiman, 1999). We therefore control for the number of employees (Firm Size), as well as the firm's age (Age). The average firm in the sample has 451 employees and is 32 years old. Because the distributions of firm size and age are highly skewed, we log-transform them. We also account for the change in Firm Size (Firm Growth), by computing the relative difference in the number of employees between the last two years. On average, the firms in our sample grew by 12 percent over two years during the observation period. As innovation activities are contingent on a firm's internal capability to exploit new knowledge and technologies (Cohen and Levinthal, 1990), we also incorporate the firm's share of employees that hold an academic degree (Share of Graduates). At the same time, this measure controls for human capital differences across firms. On average, the share of graduates among firms’ employees amounts to 26 percent. In addition, we use two dummy variables indicating whether a firm is part of an enterprise group with the headquarter being located either within or outside of Germany (Domestic Group and Foreign Group), which control for differences in governance structure and intrafirm networks, as these are known to
affect innovation activities and outcomes (Czarnitzki and Hottenrott, 2011, Hottenrott and Lopes-Bento, 2014). Furthermore, we include a dummy for whether a firm engaged in any R&D Cooperations during the last three years, which controls for external sourcing of knowledge (Cassiman and Veugelers, 2002; Laursen and Salter, 2006). In order to account for heterogeneity in innovativeness and productivity (D’Este et al., 2017), we also control for a firm’s Innovation Intensity, as measured by total innovation expenditures (in million EUR) divided by the number of employees, and Labor Productivity, i.e., total turnover (in million EUR) over employees.
Central to real options theory is the fact that uncertainty of investment projects determines firm behavior, including decisions to initiate and abandon projects (Dixit and Pindyck, 1994).
Controlling for uncertainty and a firm’s competitive environment is therefore important to make project characteristics and capabilities comparable across firms (Ragozzino et al., 2016). The MIP survey includes items on possible sources of uncertainty firms face in their main market. In particular, respondents are asked to assess the applicability of the following statements on a 4- point Likert scale (0: applies not at all, 1: applies very little, 2: applies somewhat, and 3: applies fully):
(Uncertainty Competitors) Competitors’ actions are difficult to predict.
(Uncertainty Entry) There is a major threat to the firm’s market position from entry by new competitors.
(Uncertainty Obsolescence) Products and services in the market are usually quickly outdated.
These items reflect the concept of environmental dynamism, as put forward by Miller and Friesen (1983). We decided to include them as separate control variables based on a low Cronbach’s Alpha of 0.40. Table 1 shows that firms see their competitors’ actions as the largest source of uncertainty, although the other two dimensions exhibit a higher variance across firms.
Finally, a time dummy (Year 2010, representing the respective survey wave) and a set of 21
industry dummies based on two-digit NACE categories (Rev. 2.0, Table 3) are included in the analysis to capture possible confounding effects of the macroeconomic environment and sector- specific factors.8
3.2 Empirical models
The two dependent variables in our analysis, Abandoned Projects and New Projects, are non- negative count variables. Due to overdispersion in the data (see Section 4 for further discussion), we employ negative binomial regression models (Cameron and Trivedi, 2005). Moreover, in order to allow for the possibility of error correlation between the two outcome measures, we estimate both equations as a seemingly unrelated system (King, 1989; Winkelmann, 2000).
Because several firms figure in both survey waves, standard errors are clustered at the firm-level.
4. RESULTS
Table 4 presents our main regressions. For completeness, we start with the estimation results excluding interaction terms. We find no significant relationship between Staging and Abandoned Projects in column 1 (p = 0.277). The same holds for New Projects in column 2 (p = 0.255).
Relevant for our hypotheses, however, are the results in column 3 and 4. The interaction of Staging × Resource Availability on Abandoned Projects is negative and significant ( p = 0.014) in column 3. We emphasize, however, that although we report the estimated coefficients of the non-linear negative binomial models for completeness, we cannot draw conclusions with respect to our hypotheses based on them (Ai and Norton, 2003; Greene, 2010). To do so, we plot the interaction term in Figure 1, which shows that the effect of Staging is significantly positive until a value of
8 The share of firms that stage their innovation projects does not vary substantially across industries. It is highest in electronics (72 percent) and lowest in the financial intermediation sector (50 percent, see the supplemental material to this paper for more details). Given that we control for industry affiliation in our empirical specifications, this makes us comfortable with pooling our analysis across different industries.
Table 4: Negative binomial regression results
Dependent Variable: Abandoned New Abandoned New
(1) (2) (3) (4)
Staging 0.103 -0.071 1.963** 1.459***
(0.095) (0.063) (0.801) (0.480)
Resource Availability 0.001 -0.0002 0.004** 0.003**
(0.001) (0.0008) (0.002) (0.001)
Staging × Resource Availability -0.005** -0.004***
(0.002) (0.001)
Firm Size (log) 0.348*** 0.402*** 0.345*** 0.401***
(0.034) (0.025) (0.034) (0.024)
Firm Growth 0.064 -0.042 0.055 -0.040
(0.088) (0.051 (0.086) (0.051)
Innovation Intensity 12.935*** 13.341*** 12.601*** 13.404***
(3.730) (2.063) (3.634) (2.066)
Labor Productivity 0.083 0.111 0.077 0.109
(0.070) (0.071) (0.070) (0.072)
Age (log) 0.154** -0.004 0.151** -0.003
(0.061) (0.037) (0.060) (0.037)
Share of Graduates 0.110 0.011 0.143 0.049
(0.235) (0.150) (0.234) (0.150)
R&D Cooperation 0.263** 0.157** 0.245** 0.143**
(0.106) (0.068) (0.106) (0.067)
Domestic Group 0.106 -0.013 0.109 -0.017
(0.118) (0.116) (0.117) (0.077)
Foreign Group 0.144 -0.137 0.172 -0.116
(0.155) (0.116) (0.154) (0.115)
Uncertainty Competitors -0.046 -0.065 -0.047 -0.064
(0.065) (0.045) (0.062) (0.044)
Uncertainty Entry 0.001 -0.030 -0.010 -0.033
(0.063) (0.040) (0.062) (0.039)
Uncertainty Obsolescence 0.149** 0.113*** 0.150** 0.115***
(0.059) (0.038) (0.059) (0.038)
Year 2010 -0.015 -0.269*** -0.013 -0.270***
(0.091) (0.058) (0.091) (0.058)
Constant -1.772** 0.522 -3.078*** -0.613
(0.854) (0.476) (0.944) (0.551)
Industry Dummies Yes Yes Yes Yes
ln(α) 1.289 0.006 1.282 0.001
(0.058) (0.040) (0.058) (0.040)
Notes: Cluster-robust standard errors in parentheses: * p < 0.1, ** p < 0.05, *** p < 0.01. N = 2790.
Equations (1) and (2), and respectively (3) and (4) are estimated as a seemingly unrelated system.
Figure 1: Relationship between staging and the number of abandoned projects depending on resource availability (solid line: point estimate of Staging, dashed lines: pointwise 95-percent confidence intervals)
Figure 2: Relationship between staging and the number of newly started projects depending on resource availability (solid line: point estimate of Staging, dashed lines: pointwise 95-percent confidence intervals)
Resource Availability equal to ca. 370, when the 95-percent confidence intervals starts to include zero. We interpret the fact that confidence intervals become substantially wider at the left tail of the distribution (until a value of ca. 120) as a purely statistical artifact, since there are only relatively few firms with such low levels of Resource Availability in our sample. These results provide strong support for hypothesis 1.
Column 4 of Table 4 exhibits a negative and significant (p = 0.002) interaction of Staging × Resource Availability on New Projects. Figure 2 depicts this relationship graphically. For values of Resource Availability between 100 and ca. 320, the effect of Staging is positive and significant. Then, until a value of ca. 390, confidence intervals overlap zero, from which point onwards the estimated effect turns significantly negative. This provides strong support for hypothesis 2.
In all four models presented in Table 4, confidence intervals for the overdispersion parameter ln(α) exclude one (which is equivalent to testing α=0 ). Rejecting the null indicates that the data is indeed overdispersed and, thus, that our choice of using negative binomial regressions is appropriate. Furthermore, in the supplemental material to this paper (available online) we present a graphical comparison of fit between the negative binomial and poisson model, which demonstrates that the negative binomial distribution fits the the data much better (Hilbe, 2014).
4.1 Robustness checks
Although the survey we use was collected at two points in time, only 12.8 percent of firms appear in both waves of the German CIS, which severely restricts the possibility to exploit the panel structure of the data. In any case, fixed effects models, which rely solely on the within variation in the data in order to control for time-invariant unobserved heterogeneity, are likely not applicable in our case because the between-variation of Staging in our data (std. dev. of 0.47)
is considerably larger than its within-variation (std. dev. of 0.14). At the same time, low variation in Staging over time also mitigates concerns about reverse causality in our cross- section regressions. For the context of a large multinational firm Du, Leten, and Vanhaverbeke (2014) show that R&D project managers make an active choice of whether or not to adopt a more formal management approach. This choice depends in important parts on personal preferences of the management team. If a staged approach is very stable over time (as is the case in our data) and preferences for management styles play a significant role (as shown by Du, Leten, and Vanhaverbeke, 2014), a reversed causal effect of our dependent variables on the adoption of staged project management appears to be unlikely.
Nevertheless, to reassure ourselves that endogeneity issues do not change the validity of our results, we employ a “frugal instrumental variable” method proposed by Lewbel (2012, 2018), which offers the advantage that it does not require outside instruments in order to identify the effect of endogenous regressors. Frugal IV approaches usually rely on higher-order moment restrictions in order to construct synthetic instruments from first-stage estimates (Ebbes, Wedel and Böckenholt, 2009). In the present case, the method makes use of heteroskedasticity in the errors of a first-stage regression of Staging on the remaining controls. Since Staging is a binary variable, heteroskedasticity holds naturally. The moment restrictions the model imposes in the first-stage in order to constrain parameter estimates sufficiently, such that the effects of an endogenous regressor becomes identified, are thus not restrictive. However, frugal IV approaches also have some disadvantages. Since identification is solely based on higher-moment restrictions, frugal IVs are generally seen as inferior to traditional outside instruments. In addition, Lewbel’s (2012, 2018) theoretical results are derived for a linear outcome equation, meaning that we have to give up on the count data models employed in our main estimations.9 Notwithstanding these caveats, we see the estimation approach as an appropriate second-best 9 Due to a large share of zero values, we do not log-transform our dependent variables for this analysis and instead use them in levels.
option in our case, where no suitable outside instruments appear to be available.
Using the described estimator, we find that our conclusions remain qualitatively unchanged when accounting for the possibility of endogeneity. Due to space limitations we relegate more detailed regression tables to the supplemental material and only report the main findings here.
For Abandoned Projects, the estimated interaction of Staging × Resource Availability is equal to -0.009 (p =0.057). For New Projects, we obtain a point estimate of -0.041 (p = 0.059). These results make us confident that our mains results are robust to accounting for the possibility of endogeneity, even though we cannot rule out the problem entirely.
Finally, by making use of additional survey information from the 2009 wave of the MIP, we check whether our results might be driven by differences in risk profiles and the radicalness of innovation projects between resource-constrained and resource-abundant firms. In this particular wave, firms were asked to rate the importance of several innovation objectives on a 4-point Likert scale. In line with Klingebiel and Rammer (2014), we use firms' ratings of two objectives, namely (1) to expand the range of products and services offered and (2) to enter new markets, as proxies for risk attitude and radicalness. For both measures, we find no significant correlation with Resource Availability in ordered probit regressions reported in the supplemental material.
Thus, we conclude that the moderation effect of Resource Availability on Staging cannot be explained by differences in risk profiles or the radicalness of attempted innovation projects.
4.2 Supplementary analyses
In our theoretical background section, we argued that resource availability moderates the effect of project staging on the number of newly started and abandoned innovation projects because it engenders overoptimism and managerial discretion. As the 2009 and 2011 waves of the MIP do not provide us with any measures of overoptimism and managerial discretion, we draw on additional survey waves of the MIP in order to provide further empirical support for our hypothesized theoretical mechanisms. Psychological concepts such as overoptimism by
decision-makers are generally difficult to capture in large-scale innovation surveys that focus on the firm-level. However, the 2008 and 2010 survey waves contain one interesting item that relates to future expectations, asking respondents to provide a forecast of the total innovation budget for the upcoming year. From these expectations in 2008 and 2010 we deduct the actual innovation spending in 2009 and 2011, respectively, to arrive at a measure for Forecast Optimism.10 The resulting variable has a mean of 0.304 (std. dev. = 4.568), which indicates that expectations were exceeding actual spending by about EUR 304, on average. Since calculating this measure requires us to observe firms in two consecutive years, however, our sample reduces to 1232 due to limited overlap between survey waves (639 firms answering both the 2008 and 2009 survey, and 593 firms answering both the 2010 and 2011 survey). We find a positive association between Resource Availability and Forecast Optimism ( β = 0.007, p = 0.063) in an OLS regression, employing the same set of control variables as in our main regressions (detailed estimation outputs are reported in the supplemental material). This result is in line with our argumentation that resource-abundant firms are more prone to overoptimism than their resource-constrained counterparts.
We draw on another survey question from the 2010 wave of the MIP to assess how the availability of resources correlates with the degree of managerial discretion in resource allocation decisions. The respective item in the questionnaire asks firms whether individual managers are responsible for the allocation of funding to innovation projects. Responses are recorded on a 5-point Likert scale, ranging from “does not apply at all” (coded as 1) to “fully applies” (coded as 5). The resulting variable, Managerial Discretion, has a mean of 4.3 (std. dev.
= 1.27, N = 2152), which indicates a considerable potential for discretion in resource allocation decisions. Furthermore, we find a positive correlation between Managerial Discretion and Resource Availability in both an ordered probit ( β = 0.001, p = 0.076) as well as an OLS 10 To reduce the effect of outliers, we again winsorize this variable from below and above at the 1st and 99th percentile.
regression ( β = 0.002, p = 0.005; details are reported in the supplemental material).11 These results provide evidence for our theoretical argument that resource-abundant firms tolerate higher levels of managerial discretion, which creates the necessary leeway for overoptimism to affect investment behavior.
5. DISCUSSION
By integrating insights on firms’ resource availability with bounded rationality and real options arguments, we hypothesized that resource-abundant and resource-constrained firms reap different effects – in terms of the number of newly initiated and abandoned innovation projects – from a staged investment approach to innovation. In particular, we argued that resource availability triggers overoptimism and managerial discretion, and thereby impedes adequate resource reallocation in staged innovation projects. An empirical analysis of 2,790 German firms confirms that a staged investment approach leads to a higher number of newly started and abandoned innovation projects in resource-constrained firms than in resource-abundant firms.
Supplementary analyses suggest that this is indeed because resource-abundant firms demonstrate more overoptimism and managerial discretion. We believe these findings have important implications for academic research, as well as for firms trying to improve their innovation investment decisions.
5.1 Contributions to academic research
This study provides relevant complementary insights to the literature on the economics and management of innovation, which has identified important barriers that prevent firms from initiating and successfully completing innovation projects. It proposes that by developing and 11 Due to funding reasons, in even years, MIP survey waves employ a much shorter questionnaire focusing only on the most essential items. Thus, some of the variables we include in our main specification are not available in the 2010 wave and we are thus forced to carry out these estimations with a smaller set of controls.
financing innovation projects in stages, firms can increase their learning while reducing their costs. The findings of this study show that by staging their investments in innovation, firms are able to initiate more innovation projects and also abandon a higher number of unpromising ones.
Interestingly however, this effect is only present in resource-constrained firms but not in resource-abundant firms, as the latter are characterized by overoptimism and managerial discretion, which impede adequate resource reallocation in staged innovation projects. The positive news is that for those firms that face higher barriers to innovation, i.e. that are more resource-constrained, a staged-approach to innovation projects clearly helps to overcome these barriers. As such, this study complements recent work by Danneels and Vestal (2020), who show that in order to learn from past (failed) projects, organization members need to critically analyze these projects, and do this in a climate of constructive conflict that allows for honest and open discussion. Our findings suggest that the staging of innovation projects may allow resource- constrained firms to do this, but will not suffice to overcome the cognitive biases of resource- abundant firms (D’Este et al., 2012; Leoncini, 2016; Pellegrino and Savona, 2017).
Second, this study also supports the recent evolution in the literature on the economics and management of innovation to look at general innovation performance, and not merely project abandonment as outcomes of learning from past experience and failure (Danneels and Vestal, 2020; Leoncini, 2016). As Edmondson (2011) explains, not all abandoned projects reflect bad innovation performance: experiments that lead to a rejection of previously held assumptions can in fact be very valuable. Our study advances the idea that when it comes to unpromising projects, early abandonment is actually preferable over continued investment, meaning that project abandonments can be an indicator of good rather than bad innovation performance. We hope that future studies take these insights into account when investigating the effects of learning (from failure).
Third, this study responds to a repeated call in the literature for a more contingent view on
