Korhonen and Takada 2012: Modeling the role of non-financial constraints in the development and adoption of new technologies
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Korhonen and Takada 2012: Modeling the role of non-financial constraints in the development and adoption of new technologies

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A working paper of technology evolution and adoption under constraints. Contains a case study of copper industry and a NK-based simulation experiment. Results: constraints do not drive the development ...

A working paper of technology evolution and adoption under constraints. Contains a case study of copper industry and a NK-based simulation experiment. Results: constraints do not drive the development of new technologies, but they can drive the adoption of already existing non-mainstream technologies.

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    Korhonen and Takada 2012: Modeling the role of non-financial constraints in the development and adoption of new technologies Korhonen and Takada 2012: Modeling the role of non-financial constraints in the development and adoption of new technologies Document Transcript

    • Modeling the role of non-financial constraints in the development and adoption of new technologies Janne M. Korhonen (primary and corresponding author, responsible for background, theory, analyzing the results of simulations and writing the article) janne.m.korhonen@aalto.fi Mikito Takada (responsible for coding the simulations) mikito.takada@aalto.fi Suggested key words: Technology adoption; Innovation; Constraints; Environmental innovation; Agent-based models; NK models; Copper miningEarlier versions of this paper have been presented at PREBEM 2011 conference in Rotterdam and accepted for presentation at Organization Science Winter Conference 2012 pre- conference event in Colorado Springs.AbstractThis paper studies the effects of non-financial constraints (e.g. resource scarcities) on thedevelopment and adoption of technologies. We build a simulation model of technologydevelopment and adoption under constraints, and compare it to a case study from coppermining industry. Rather than focusing on “jury-rigged” temporary solutions, we areinterested in innovations that remain in use even after the constraint has been lifted. Theresults suggest that unexpected constraints only rarely lead to lasting innovations as such,but they drive firms to adopt previously developed, more efficient technologies andaccelerate convergence to a dominant design. The effects are moderated mostly bycompetitive intensity and the complexity of technologies. The results help to reconcile twoopposing viewpoints on the role of constraints and have public policy implications in e.g.the success measures of “green growth” initiatives.IntroductionDoes necessity give birth to new, improved technologies? A burst of recent researchsuggests that under certain circumstances, innovations (defined in this paper as theimplementation of ideas, procedures and structures that are novel to the organization; e.g.Amabile et al., 1996) can be facilitated by introduction of constraints (Garud and Karnoe,2003; Gibbert and Scranton, 2009; Gibbert and Välikangas, 2004; Gibbert et al. 2007;Goldenberg et al., 2001; Hoegl et al. 2008, 2010; Moreau and Dahl, 2005; Peck et al. 2010;Popp et al., 2011; Srinivas and Sutz, 2005; Starr and MacMillan, 1990; Välikangas andGibbert, 2005, Weiss et al., 2011). The conclusions of these studies broadly agree thatconstraints (discontinuities that force firms to unexpectedly change its accustomed workingpractices, e.g. tightening regulation, pollution limits, and resource shortages) may facilitateinnovation in some but not all conditions. For example, economic historians Rosenberg(1976) and Hughes (1983) have argued that innovations tend to be made in response to“bottlenecks” or technological “reverse salients,” and research on environmental innovationhas found that regulatory constraints are the primary driver of innovation (Popp, 2006; Poppet al., 2011). Although meeting tightening regulatory and other challenges will clearlyrequire firms to adopt new technologies, the question whether constraints, as a rule, either   1  
    • benefit or hinder technological development remains open. If we define technologicaldevelopment as a process towards increasingly efficient-for-purpose solutions to a givenproblem, do constraints lead to less or more efficient solutions? In addition, studies on therole of constraints potentially suffer from a selection bias, as only successful developmentstend to survive to be studied.In fact, examples of innovation that have been clearly motivated by constraints but haveremained in use afterwards remain relatively scarce. In many cases where an inventionseems to have originated from a specific constraint, an argument can be made that (a) itsorigins may be in development work done prior to constraint, even if the constraint wasunexpected (see e.g. Arthur, 2009; Schoenmakers and Duysters, 2010) and (b) its continuoususe may have been dependent on subsequent improvements and modifications that wouldhave not been possible under conditions of original constraints. Arguably, some constraintscan open previously blocked development paths and lead to overall higher performance.Equally arguably, other constraints may result to paths that lead to lower performance.This paper is positioned to extend the existing research on the role of constraints ininnovation (e.g. Hoegl et al., 2008, 2010; Gibbert and Scranton, 2009; Gibbert and Välikangas,2004; Gibbert et al. 2007; Weiss et al., 2011), on the origins of radical innovations (e.g.Schoenmakers and Duysters, 2010), and on regulation-driven innovation (e.g. Popp, 2006;Popp et al., 2011). The main contribution of this paper is in developing a model for assessingwhether constraints – as a rule – tend to have positive or negative influences on thedevelopment and adoption of novel technologies. The focus of this paper is on the mostinteresting cases of constrained innovation: those where the innovation remains in use evenafter the constraint is removed. A linked contribution is the assessment of the role ofconstraints in the emergence of new technological paradigms (Dosi, 1982).In addition, the micro-level dynamics of technology development and adoption as a result ofconstraints are not clear. If constraints spur innovation, the effect of constraints should be toincrease the variety of technologies in use, at least temporarily. The evolutionary model oftechnical change (e.g. Dosi, 1984; Nelson and Winter, 1982; Sahal, 1981) suggests that as oldpath dependencies are loosened and dominant designs suffer from discontinuities, theresulting “era of ferment” should bring about a period of technological experimentation (e.g.Van de Ven and Garud, 1993; Abernathy, 1978; Basalla, 1989). Research so far has attributedthis variety creation either to exogenous stochastic technological advances that “extend” thespace of possible solutions (Dosi, 1982; see also Kaplan and Tripsas, 2008) or to differences incognition guiding actor’s framing and interpretation of the technology (Kaplan and Tripsas,2008). The role of exogenous “limiting” discontinuities, i.e. constraints, is less studied. Onthe other hand, the extent of technological experimentation should be moderated by thecompanies’ need to quickly adopt workable solutions to their pressing problems. Therefore,this paper attempts to model the interaction of these effects and determine which of the twoopposing forces - the variety-increasing fermentation or the variety-decreasing competitivepressure – tend to be dominant in a discontinuity caused by the introduction of constraints.The paper is structured as follows: first, we discuss prior research into the nature ofconstraints and innovation. Second, we introduce a case study from copper manufacturingindustry. This study serves as a context and as an example for a simulation model of theeffects of constraints on innovation. We then compare the assumptions and the results in thesimulation to empirical observations. Finally, a discussion and conclusions is provided.   2  
    • The Nature of Constraints: some prior explorationsThe role of constraints in innovation has been acknowledged for long in maxims such as“necessity is the mother of innovation,” but research remains surprisingly rare. Constraintsare usually lumped together with other exogenous events that alter the environment wherefirms operate. Explicit research on constraints has divided to two streams of research. The“resource slack” research, focusing mostly on financial constraints, tends to either seeconstraints as negative, limiting factors that bound individual and organizationalperformance and limit exploration (e.g. Amabile, 1996; Ancona, 1990; Camison-Zornoza etal. 2004; Damanpour, 1991; Gassman and von Zedtwitz, 2003; Nohria and Gulati, 1996). Thisresearch stream makes an argument that innovators’ access to financial resources – resourceslack - is a key determinant of its performance.The “breaking the barriers” research stream, on the other hand, sees constraints as “focusingdevices” (Bradshaw, 1992; Frenken, 2006) that help individuals and organizations focus theirattention and cognitive resources to overcome technological “bottlenecks” (Rosenberg, 1976)or “reverse salients” (Hughes, 1983) that hold up technological progress. According to thisview, constraints force otherwise complacent firms to rethink traditional practices and adoptnew ways of working (Arthur, 2009; Gibbert and Scranton, 2009; Gibbert and Välikangas,2004; Hoegl et al., 2008, 2010; Katila and Shane, 2005; Mone et al., 1998; Nonaka and Kenney,1991; Srinivas and Sutz, 2005; Välikangas and Gibbert, 2005; Weiss et al., 2011). In support ofthis view, research on environmental innovation has noted that regulatory constraints arethe key drivers of innovation in many industries (Popp, 2006; Popp et al., 2011).However, the research findings are far from conclusive: for example, empirical evidencesuggests that abundant financial resources can also lead to unhealthy complacency andfavor incremental adaptation and exploitation instead of exploration (Mishina et al., 2004;Tan and Peng, 2003). Similarly, the beneficial effect is tempered by e.g. increasedcomplexities introduced by additional design requirements (Arthur, 2009).The diverging views have caused commentators to explain that innovations can happenbecause of or despite constraints (Gibbert and Scranton, 2009; see also Weiss et al., 2011 andHoegl et al., 2010). A careful reading of literature suggests that these two, superficiallyincompatible views can be reconciled. Constraints come in different forms, and whileexogenous non-financial constraints may very well cause eras of ferment that spurinnovation, financial constraints may make exploitation of these explorations difficult. Muchof the rest of this paper will explore the assumption, focusing on the non-financialconstraints and competitive dynamics.Historical case study: resource constraints and the development ofautogenous copper smeltingIn the first half of the 1900s, copper smelters used energy from coal, oil, gas or electricity tosmelt the ore. To improve the efficiency of the process, experiments in utilizing the latentenergy of sulfurous metal ores had been carried out since the 1870s, with first patents beingawarded in 1897 (Inco, 1955). In theory, by burning sulfur one might dispense with externalenergy altogether. Some pilot plants with simple sulfur-burning or “autogenous” furnaceswere in fact built in the early 1900s (Newton and Wilson, 1942; Särkikoski, 1999; Inco, 1955)but the technology’s limitations, uncertainties over recouping high fixed costs inherent inindustrial-scale trials, and a general lack of concern for energy efficiency prevented thetechnology from taking root in the industry. Largely for these reasons, technologicalprogress within copper smelters was limited to minor improvements on existing furnaces.   3  
    • Birth of flash smeltingWhen research resumed after the interruption of the Second World War, autogenoussmelting was “discovered” again. In Canada, mining giant Inco pursued increasedproductivity for its huge smelters (Saddington et al., 1966). Building on its pre-war research,it quickly homed on newly available oxygen generators. Inco’s solution was essentially anincremental improvement to its existing “reverberatory” furnaces: instead of fossil fuels,tonnage oxygen was injected to ignite sulfur in pulverized ore concentrate. This almostcompletely eliminated the need for fossil fuels, increased productivity, and reducedemissions (Queneau and Marcuson, 1996). The resulting “Inco flash furnace” wasdemonstrated on commercial scale in January 1952 (Queneau and Marcuson, 1996). As oneof the largest mining companies in the world, having essentially inexhaustible resources andoperating in a country untouched by bombings, Inco faced few to no constraints in R&D.In contrast, a small, state-owned Finnish copper company Outokumpu was in dire straits.Just before the war, Outokumpu had invested heavily in electric furnaces, completing theworld’s largest electric copper smelter in 1936. Unfortunately, one of the demands of the1945 peace treaty stipulated that two large hydropower plants had to be ceded to the SovietUnion. The resulting electricity shortage was an existential threat to Outokumpu(Särkikoski, 1999; Habashi, 1993), The crisis gave precious little time to conduct extensiveresearch; furthermore, Outokumpu’s access to advanced technology was severely curtailedby post-war disruptions and currency limitations. These effectively precluded foreigntechnology purchases (Särkikoski, 1999; Habashi, 1993; Bryk et al., 1958).Fortunately, familiarity with pre-war research and practice provided them with componentsnecessary for a “novel” innovation. Developed in just about three months in late 1945,Outokumpu flash furnace integrated a pre-existing idea - burning pulverized oreconcentrate - with components and know-how from coal power plants and sulfur-extractingroasting furnaces (Särkikoski, 1999). Instead of unavailable, electricity-dependent oxygengenerators, Outokumpu’s furnace design used waste heat from outgoing flue gases topreheat incoming air. Sulfurous ore could then be injected to the hot air and ignited.Outokumpu’s flash furnace was demonstrated in commercial scale in February 1947.Ironically, the electricity rationing that had inspired Outokumpu’s development efforts waslifted just three months before the first flash furnace went operational.Spread of flash smelting: emission controls and the 1973 energy crisisThe first licensee for Outokumpu was the modernized Ashio smelter in Japan, whichcontracted for a complete turnkey delivery in 1954. After this initial reference, two majorconstraints facilitated the spread of Outokumpu’s technology. The first constraint becameapparent in the early 1960s, as copper industry was increasingly put to account for itsharmful emissions. Starting from Japan, many copper producers concluded that modifyingtheir existing furnaces to comply with tightening emission controls would be uneconomical.Outokumpu’s solution provided easier emission control, and as a bonus, would increaseenergy efficiency as well. This resulted to sales to nine smelters (see Figure 1).The second major constraint was the 1973 energy crisis. While environmental regulationswere tightening as well, this time the key driver was energy efficiency. Combined, these tworeasons caused ten additional smelters to adopt Outokumpu furnaces between 1973 and1990 (Särkikoski, 1999). At the same time, the old mainstays (reverberatory, blast and electricfurnaces) became uneconomical for energy and environmental reasons. This led to aconsolidation of industry and left Outokumpu’s furnace the dominant design in newer,larger copper smelters.   4  
    • Figure 1. Technologies used at major copper smelters, 1930-1990. Data from Biswas andDavenport (1976), Davenport et al. (2002), and U.S. Bureau of Mines Minerals Yearbooks,1933-1990. (Note: As furnace sizes vary and a smelter may operate more than one furnace,the graph shows smelters using particular furnace technology, rather of the total number offurnaces.)In contrast, Inco was never particularly interested in technology sales. In fact, for the first 25years Inco did not license its technology to anyone. This was largely due to a potential threatfor Inco’s mainstay nickel business, as the furnace type was particularly economical fornickel smelting as well (Brundenius, 2003). Another likely factor was that income fromtechnology sales would have been barely visible on giant Inco’s balance sheet; for example,the contract with Ashio smelter was worth only $156 000, or around $1.3 million in 2011money (Särkikoski, 1999). It is worth noting, though, that Inco’s method did remaintechnically superior up until to the 1970s, with lower overall energy consumption andhigher productivity (Biswas and Davenport, 1976). However, when Outokumpu introducedoxygen injection in the 1970s, the technological gap nearly disappeared (Figure 2).   5  
    • Figure 2. Efficiency and productivity developments in copper furnaces (Biswas andDavenport, 1976; Bryk et al., 1958; Davenport et al., 2002; Saddington et al., 1966).Why, then, other manufacturers failed to develop flash technologies? Based on the evidence,the most probable reason seems to be that they lacked a compelling reason to do so. In acommodity business where investment costs are high and competition intense, developingor adopting untested processes represents a substantial risk for the operator. As previousresearch (e.g. Popp, 2006; Popp et al., 2011) has shown, regulatory or other constraints wereusually needed to persuade producers to develop or adopt new technologies.It is very likely that Outokumpu itself wouldn’t have taken the gamble without theconstraint imposed by the post-war energy shortage. In fact, it is uncertain whether it wouldhave even adopted the technology, had the electricity rationing simply been lifted before thefirst flash furnace was completed. A comparison can be made to Boliden, in many ways asimilar copper company in neighboring Sweden. When Boliden redesigned its Rönnskärsmelter in 1948, it simply adopted tried and tested electric furnaces supplied with abundantelectricity from Swedish hydropower plants (Herneryd et al., 1954).Modeling constraints with an NK frameworkAlthough the case study strongly suggests that constraints can advance technologicalchange, the evidence on the overall effects of constraints is inconclusive. Developing propercase studies is hampered by the lack of reliable accounts describing the birth of constraint-induced innovations. What studies there are tend to focus on “success stories,” raising aquestion of selection bias. Furthermore, when studying the effects of constraints on theevolution of technology, the theoretical focus is on longitudinal and nonlinear dynamics.These conditions make simulations an attractive methodology (Davis et al., 2007, p. 481)To experiment with history, we have created a simplified model of innovation anddisruptions caused by introduction of constraints. The model is based on NK fitnesslandscapes, originally introduced by Anderson (1983; 1985) and famously adapted byKauffman (1993) to the study of evolutionary biology. The NK model has been previouslyused in important work in strategy and organizational literature (Levinthal, 1997; Levinthaland Warglien, 1999; Rivkin, 2000; Rivkin and Siggelkow, 2002). The model has also been   6  
    • previously used to study innovations in a manner very similar to this present work (e.g. Almirall and Casadesus-Masanell, 2010; Ethiraj and Levinthal, 2004; Frenken et al., 1999; Frenken, 2006). Although simulation studies of innovation are becoming more common, we do not know of prior efforts to simulate explicitly the effects constraints have on innovation. Modeling the effects of constraints In order to study the effects of disruptions due to constraints in as simple model as possible, we consider technologies that utilize N components. These components represent the decisions and trade-offs that the designer needs to take account when designing a technology, e.g. different materials, differing energy sources, different subsystems, different requirements, or different ways of organizing a group of people. Although a more general model (e.g. Valente, 2008) would allow for more than two states per component, the common binary (0/1) assumption results to the simplest and most tractable model without undue loss of generality. Therefore, in our model, the simulated organizations have to make decisions concerning N components, with each decision having two options. Each combination of N options is assumed to lead to a different performance of the technology. In other words, there is a mapping between the “design” of a technology and its performance. We consider “performance” to be an abstract value that denotes the “goodness” or “fitness” of the technology, for example, its productivity. As is the convention in the literature utilizing NK models, for reasons of simplicity most parameters can be considered exogenous and fixed for the duration of each simulation run, without detracting from the simulation’s usefulness (see e.g. Sorenson, 2002). Representing technology and its performance in the NK model Formally, a technology is represented in the NK model by a string R of N components (r1, r2, … rN), where each ri represents a choice of {0, 1}. These realized choices contribute to the overall fitness, or performance, W of the technology so that each possible choice configuration Ri has an individual Wi. The complexity of the technology is modeled with a variable K, which determines the “interconnectedness” of the components. Thus, the contribution wi of each resource ri depends on K other components. For each of 2K possible combinations, a value is drawn from a uniform distribution over the unit interval [0, 1]. The overall performance W of each individual technology string is the average over N value contributions, and each individual technology string has a specific performance W. N ∑ w(r ;r ,...,r 1 i1 iK ) i=1 W (r1,...,rN ) = N (1)€ In the equation, rij j = (1, … , K) are the combinations of the K components that are interconnected with the component ri. As is the norm in the literature, in absence of specific knowledge, we assume that interdependencies are determined randomly. Search strategies: incremental (local) and radical (long jumps) In short, the model represents a possibility space where individual firms search for the best possible technology among 2N possible resource configurations. In practice, the search proceeds by the firms varying the configuration of one resource at a time. There are various search strategies the firms could possibly use in this landscape, but we assume that firms look at the alternatives that are one step away from their current position, and then select the first alternative that increases the technology’s performance. If the firm cannot alter any   7  
    • configuration without decreasing the performance, the search will stop. This local search isthe simplest search heuristic that captures the idea that firms cannot know in advance whichcombinations of features would eventually provide the best overall performance. Althoughmost literature assumes that firms look at all the alternatives and select the best one (so-called “greedy search;” see e.g. Frenken, 2006), we feel that our assumption captures slightlybetter the incremental, somewhat unplanned and uncertain nature of technologicaldevelopment and technological path dependency (e.g. Dosi, 1982; in addition, Ward, 1994proposed a “path of least resistance” model of creativity which leads to the sameconclusion). Because this local search stops once there is no more room for improvement, itis prone to get trapped into local maxima when the mapping is complex. This represents theeffects of path dependency and the difficulties firms have in “switching the horse” due tofinancial and psychological reasons. As an example drawn from the case study, severalauthors (e.g. Särkikoski, 1999; Biswas and Davenport, 1976) have noted that copper smeltershave historically been reluctant to radically alter their operations before the need becomescompelling.We have also modeled an alternative to local search, called “long jumps.” These representradical innovations, and are initiated if a firm finds its technology’s performance to be lessthan a predetermined percentage of industry average. As usual in the literature, radicalinnovations are represented by the firm randomly drawing a new string r + R. The length ofnew string r is varied in different simulations so that 1 ≤ r ≤ R, i.e. r components are changedsimultaneously and (R – r) components are retained. This variable length of the new stringrepresents different “search distances,” and can be conceptualized as the breadth of ideasdesigners can use when trying to design around the constraint. If the new string’sperformance is an improvement to old string, it is adopted.Modifications to the basic NK model: competition and constraintsWe have added two modifications to this general NK framework to model the dynamics offirms competing in the same marketplace and effects of constraints. First, to model thecompetitive dynamics affecting the development of technologies, we allow better-performing technologies to be imitated. The imitation is initiated similarly to long jumps. Ifa firm’s technology lags sufficiently behind the industry average in performance, it choosesrandomly between long jump and imitation. As is customary in the literature (e.g. Kauffmanet al., 2000; Ethiraj and Levinthal, 2004; Almirall and Casadesus-Masanell, 2010), theimitating firm chooses a better-performing technology at random, with the likelihood of aparticular technology being chosen being proportional to its performance. In other words,better technologies have a better chance to be copied and thus they spread in themarketplace. It is worth noting that our model is equivalent to a model where firms whoseperformance is sufficiently poor exit the market and are replaced by new entrants eitheradopting a “best available” technology or developing new technologies.Second, the effect of constraint is modeled by forcing the firms to set the configuration ofone (or more) component to “1” for the variable duration of the constraint. This represents asudden lack of some critical component, introduced by some exogenous supply shock suchas material shortage, war, or changing regulation. As long as the constraint is in force,imitation and long jumps are limited to strings that comply with the constraint. Otherwise,firms are free to long jump and copy any string that fits the criteria outlined above.This simple model corresponds to an absolute resource constraint, where a particularcomponent (i.e. configuration “0”) is no longer available at any price. Although many real-life constraints are not absolute, and instead only increase the price of a component, webelieve our abstraction captures the essence of constraint’s impact - that a firm needs to   8  
    • develop an alternative - in a simple yet effective way. In the view of example case presentedabove, we think this simplification is defensible.Finally, we are especially interested in the performance of novel technologies once theoriginal constraint has been lifted. To this end, the constraints last only a variable number ofturns, after which the firms are free to change the constrained component’s configuration.Simulation mechanicsFor this paper, we have set N = 16. We completed multiple series of simulation sequenceswith varying parameters for imitation propensity, long jump search length, and complexityK of the technology. Each simulation sequence contains 50 individual simulation runs foreach K = 0…15. The performance landscape and starting technologies for 100 individualfirms are determined randomly for each run. During runs, firms search the landscape for thetechnology that offers the highest performance (local maxima), measured by fitness W. Theconstraint is introduced at turn 15, when all firms have already found their local maxima.The constraint forces all the firms to alter the state of one predetermined component to “1.”In these simulations, the constraint lasted five turns and was lifted at turn 20. All thesimulation runs had found a new, stable local maximum by turn 30.Each simulation sequence therefore includes 800 simulation runs, 50 for each K value.Simulation series consist of multiple sequences, where other parameters are altered. Themain parameters altered between simulation series are represented in Table 1. We alsoexplored the simulation’s robustness to parameter changes and possible hiddennonlinearities. These robustness checks included testing with variable constraint durations,variable number of organizations (10, 50, 100, 150 and 200), and with more than onecomponent’s configuration constrained. We also tested the simulation’s behavior when thesearch dynamic was replaced with random mutations. In addition, the simulation’s behaviorat close to extreme values of imitation/long jump threshold (1% and 99%) was tested. Thesimulation’s behavior appears linear and the results are broadly robust to these changes. Theexception is that random mutation search produced a slightly different performance bias.This difference will be discussed below.Table 1. Simulation parameters.Simulation series 1. Varying imitation 2. Long jumps threshold allowedImitation/long jump threshold* 0-100%, 5% increments 75%Long jumps enabled No YesLong jump search distance - 1-16Simulations sequences 21 16* = Imitation/long jump threshold is the percentage of average performance that triggers eitherimitation of more successful technology or a long jump. If long jumps are enabled, the firm has anequal probability either to imitate or long jump.The simulation was build by extending a freely available Sendero NK model (Padget et al.,2009) in RePast/J environment. Source code and the data can be obtained from the authors.   9  
    • Simulation resultsAs the core interest of this paper is to examine how unexpected constraints affect theevolution of technology, we begin by comparing the effects of constraint on averageperformance achieved over multiple simulation runs. We then describe how changes inparameters affect the odds of positive or negative change as a result of a constraint. We alsostudy the role of constraints in the emergence of novel technologies. Finally, using entropystatistics, we analyze the effects constraints have on the variety of technologies in use. Dueto large number of individual simulation sequences and relative robustness of the results,this paper concentrates on six representative simulations. The simulations and theirparameters are described on Table 2 below.Table 2. Summary of parameters of representative simulations.Simulation name Imitation Long Long jump threshold jumps lengtha) No imitation allowed - No -b) Imitation threshold at 75 % 0.75 No -c) Always imitate 1.00 No -d) Radical innovation, short search 0.75 Yes N/8 (2)e) Radical innovation, medium search 0.75 Yes N/2 (8)f) Radical innovation, distant search 0.75 Yes N (16)The effects of constraints on average performanceFor all individual simulated firms in each run, we calculated the difference AFT – BEF,where AFT denotes the firm’s technology’s average performance after the constraint (at turn30) and BEF the average performance before the constraint (at turn 14). This results to adataset of 5000 observations per K value for each simulation setting.To determine whether constraints generally are beneficial or harmful, we calculated theaverage and standard error of these observations for each K value. The results for sixrepresentative simulation sequences are plotted in Figure 3. The left-hand vertical axismarks the change in average performance AFT – BEF as a percentage of averageperformance before constraint. The horizontal axis represents the K value. Error barsindicate standard errors of the mean, black dotted lines indicate one standard deviationfrom the mean, and the red horizontal lines mark the level of zero change. Finally, thick redsolid line and right-hand vertical axis measure average performance after constraint.As can be seen, on average, constraints have a very slight but positive effect to theperformance of simulated technologies. However, the changes are only rarely statisticallysignificant. In addition, variability – as indicated by the dotted line marking one standarddeviation from the mean – is relatively high for all but the simplest technologies (low Kvalues). The interpretation is that constraints have a nearly equal likelihood of either helpingor hindering technological progress, but they come with a slight positive bias. The finding isconsistent with the dichotomous nature of previous literature on the effects of constraints(e.g. Hoegl et al., 2008; Gibbert et al., 2007; Välikangas and Gibbert, 2005; Weiss et al., 2011).The results are very robust over a wide range of simulation parameters. Changing the searchdynamics to random search produces essentially the same result, although the bias thenbecomes slightly negative and variance increases. The only notable anomaly observable inthe results is that both the largest losses and greatest gains in average performance wereseen in the simulations where competition was most intense (imitation threshold near or at100%). Three factors account for this difference: first, more homogenous industries have less   10  
    • variety to start with. When a constraint is imposed, lack of variety means lack of options thatcould act as platforms for further development. Second, more intense competition does notgive time for technologies to fully develop. Even a promising technology path is likely to beabandoned, if it doesn’t provide clear and immediate benefits. As a result, breaking the pathdependency and developing truly novel alternatives becomes difficult. Thirdly, however,intense competition also ensures that if the constraint leads to a performance breakthrough,it is speedily adopted throughout the industry. a) No imitation allowed d) Radical innovation, short search 1.0 1.0 4 4 2 2 Change (percent) Change (percent) 0.8 0.8 0 0 -2 -2 -4 -4 0.6 0.6 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 K value K value b) Imitation threshold at 75% e) Radical innovation, medium search 1.0 1.0 4 4 2 2 Change (percent) Change (percent) 0.8 0.8 0 0 -2 -2 -4 -4 0.6 0.6 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 K value K value c) Always imitate f) Radical innovation, distant search 1.0 1.0 4 4 2 2 Change (percent) Change (percent) 0.8 0.8 0 0 -2 -2 -4 -4 0.6 0.6 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 K value K valueFigure 3. Mean change (thin line with error bars) and average performance (thick line)after constraint. Error bars indicate standard error, dotted line one standard deviation.Odds of change: how likely are the improvements?To gain more detail about the effects of constraints, we plotted the likelihood a constraintwould spur an individual firm to improve its technology. The odds of change weredetermined by calculating the number of observations exhibiting positive, negative or nochange when comparing performance before and after the constraint. The results,cumulatively plotted to two tables, are presented in Figure 4 below.   11  
    • In each plot, the area below lower lines represents the percentage chance of constraint toresult to performance decreases. The area above upper lines represents the chance for theconstraint to increase performance. The area between two sets of lines represents the chancethat constraint has no effect in performance. The dashed horizontal line denotes 50% chance.The plots reveal that the probability for both positive and negative change increases ascomplexity K increases. Increasing the imitation intensity parameter above 80% (imitationthreshold) produces results that differ most from the overall trend. In all cases, the mostprobable outcome is no performance change.Allowing firms to search farther for radical innovations does not have an appreciable impacton the probabilities. As can be seen from the near-identical plots, the odds are largelydetermined by imitation threshold (set to 75% in radical innovation simulations). In short,more options may not translate to better odds, and as Figure 3 above shows, there is little tono direct performance benefit either.Figure 4. Odds of change. Left panel with varying imitation thresholds, right with varyingradical innovation search lengths. Variance on left panel is exclusively from highimitation threshold (>70%) simulations.The influence of constraints on the emergence of novel technologiesIn many simulations, the technologies that were ultimately used to “go around” theconstraint were not developed as a response to the constraint: often, simulated firms hadused them even before the constraint was introduced. To determine the extent to which pre-existing designs were used and the likelihood of novel technologies emerging as a result ofconstraint breaking the technological path dependency, we compared the technologies inuse before the constraint to technologies being used at the end of the simulation. The resultsof this comparison, along with average performance achieved, are plotted on Figure 5below. In the plots, black line represents the percentage of pre-existing technologies out ofall technologies in use at the end of the simulation (i.e. 100% indicates that all technologiesin use at the end were in existence before the constraint). Error bars indicate standard errorsof the mean, and dotted black line marks one standard deviation.   12  
    • a) No imitation allowed d) Radical innovation, short search 100 100 80 80 % pre-existing technologies % pre-existing technologies 60 60 40 40 20 20 0 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 K value K value b) Imitation threshold at 75% e) Radical innovation, medium search 100 100 80 80 % pre-existing technologies % pre-existing technologies 60 60 40 40 20 20 0 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 K value K value c) Always imitate f) Radical innovation, distant search 100 100 80 80 % pre-existing technologies % pre-existing technologies 60 60 40 40 20 20 0 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 K value K valueFigure 5. Share of pre-existing technologies out of all technologies in use after a constraint.Error bars indicate standard error, dotted line one standard deviation.The results indicate that in general, well over half of the technologies used at the end of thesimulation existed before the constraint. In environments where imitation is intense, over80% of technologies may be pre-existing, although variability tends to be very high: caseswhere no technologies were in use before constraint are relatively common, as well. Asexplained previously, the dynamics of intense competition can produce both results, andthus help explain the dichotomous nature of previous explorations.As was the case with performance, allowing radical innovation does not greatly influencethe results. It is also worth noting that the results are statistically more robust than theresults on average performance. Interestingly, the results are robust to the number oforganizations (i.e. initial starting positions).The figures only include technologies that were identical to those existing before theconstraint. This further underestimates the importance of pre-existing technologies, asobservations of individual simulations suggest that firms may copy an existing “constraintimmune” technology at the beginning of the constraint, and then develop it into a novel   13  
    • technology. However, measuring this effect and differentiating between imitation andindependent but convergent development was not feasible with the current data set.These findings suggest that constraints are more likely to cause firms to adopt existingtechnologies than to spur them to develop radically new technologies.The effect of constraints on technological varietyA positive change in average technological performance can result from either firmsdeveloping new, superior technologies, or from imitating existing (or recently developed)technologies from other firms. The intensity of imitation can be analyzed by calculating theentropy statistics for the technologies in use before and after the introduction of constraint(Frenken, 2001).Entropy statistics applied to frequency distributions of individual “designs” (16-bit strings)can describe the evolution of a technology in terms of the changes in the degree and natureof product variety (Frenken, 2001:138). The entropy of the distribution of designs indicatesthe extent to which the designs within an “industry” (individual simulation) arecharacterized by “dominant designs.” Maximum entropy corresponds to the case in whichall designs occur at the same frequency, and minimum entropy (zero) to the case in whichthere is only a single design that is used by all firms in the simulation. We calculate theentropy H of an N-dimensional distribution (Theil, 1972: 116, 155) as follows:where ij…w are individual components and pij…w is the frequency at which a design withcomponents ij…w occurs. The entropy H is measured in bits.The results for representative simulations are displayed in Figure 6 below. The figures plotmean entropy during simulation runs for five component interdependency settings (K = 0, 4,8, 12 and 15). Hatched area indicates the period during which constraint is applied (turns 15-20). Standard errors of the mean are too small to be visible.The plots show that no-interdependence technologies (K = 0) reach quickly the globaloptima, i.e. single optimum configuration, where entropy is zero. As interdependence Kbetween components increases from zero, the variety of feasible designs increases as well.Applying the constraint reduces the variety of feasible designs. In simulations whereimitation is allowed, constraints also create an incentive to copy better-performingtechnologies, further reducing variety. Panel c) in Figure 6 illustrates what happens whencompetition is extremely intense: only a few designs, at most, survive even before theconstraint, and fewer still after. In short, constraints lead to a marked decrease in thetechnological variety, especially when competition is intense (high imitation trigger value).   14  
    • Figure 6. Mean entropy (average variety of technologies in use) in representativesimulations. Hatched rectangles indicate turns when constraint is in force.Robustness and limitations: comparison with the case studyThe robustness, validity and generalizability of simulation studies should always beassessed carefully. We believe that the basic assumptions (technological development as amyopic process, path dependency, the role of constraints, and competition) made in thisstudy hold relatively well for many real-life contexts. In the following, we first compare theassumptions made in the simulations to observed historical dynamics. We then compare theresults of the simulations to observed realities.   15  
    • Comparison of simulation assumptions to historical dynamicsThe comparison of assumptions made in the simulation model with the dynamics observedin reality shows that the assumptions seem to match reality relatively well. Based ontechnical literature (Biswas and Davenport, 1976; Davenport et al., 2002; Kojo and Storch,2006; Kojo et al., 1999; Queneau and Marcuson, 1996; Saddington et al., 1966) and interviewswith copper metallurgists, copper furnaces can be conceptualized as a technology withapproximately 16 important design dimensions, or components (N), with 2-3 alternativesand roughly 3-5 important interdependencies (K) between components. The dimensions onTable 3 below can define all the copper smelting technologies used between 1930 and 1990.For the most part, technological search in the copper industry was “local” and concentratedon incremental improvements of existing technologies. Investments in existing technologiesand high capital costs meant that firms had to have considerable incentive before changingtheir technologies. The performance of competing solutions was relatively straightforwardto measure, for example as tons of processed concentrate per day or as cost of copperproduced. Firms whose technologies were not performing did license or copy bettertechnologies. Although the imitation threshold level that corresponds to the historical case isimpossible to quantify exactly, we feel that a plausible case can be made for a realisticthreshold level being in the 75-95% range for the most of the time period under study.Finally, real-life constraints clearly had uneven effects on technologies used.Table 3. Copper furnace design dimensions. Approximate N ≈ 16, K ≈ 4.Design dimension Primary alternativesFurnace direction Horizontal, verticalFurnace shape Rectangular, roundSmelting reaction type Bath reactions, suspension reactionsEnergy source Fossil fuels, electricity, autogenousGas blow Not used, burners/tuyeres/lancesBlast preheat Not used, usedOxygen injection None, enrichment, significantOre granularity requirements None, fine ore requiredOre moisture requirements None, dry ore requiredWaste heat recovery Not used, usedSulfur dioxide (SO2) recovery No concern, necessaryOffgas SO2 concentration Dilute, concentratedSO2 leaks No concern, should be minimizedDust evolution No concern, should be minimizedCooling Convection, watercooledTime between refurbishments Less than 1 year, up to 2 years, longer   16  
    • Comparison to simulation resultsThe case briefly outlined above can be thought to represent three consecutive resourceconstraints – post-war energy scarcity, introduction of emission control regime, and the 1973energy crisis. The dynamics represent one relatively typical outcome observed insimulations where imitation threshold is moderate to high (over 70%). In these simulations,it is common for one or two “superior” technologies to emerge before the constraint.However, most firms lack an incentive to adopt these immediately. Only firms unable tocope with their existing technology, like the Ashio smelter in the case study, adopt it.Constraints can often change the situation. In simulations where a superior technologyexists and remains unaffected by the constraint, there is a high likelihood that firms will tryto imitate it. In these cases, it is typical for average performance to increase. Furthermore,this new technology may offer new opportunities for further development, which increasethe performance even further. The parallels to the case study above are obvious.The validity of the simulation seems to be enhanced by the fact that parallels to all threeconsecutive real-life constraints and their dynamics can be found from the simulations. Forjust one example, see Figure 7 below. It details a simulation run where resource constraintresulted to the abandonment of old technologies, and to the emergence of improvedtechnology. The parallel to the Outokumpu case would be even more evident if simulatedfirms had not been able to “license” technology 2, and had instead turned to technology 1.Figure 7. Example of simulation dynamics: constraint eliminates one technology, damagesanother, which then improves to become the best-performing technology.Limitations of the studyBased on the above comparisons, we feel that our simulation captures the essentialdynamics of many process industries (e.g. mining, metals manufacturing and energygeneration), where the performance of a technology is relatively straightforward to measure(e.g. processing capacity) and high capital costs hinder technological experimentation.However, the results may not be generalizable to industries where experimentation is easierand/or performance measurements are more ambiguous. In addition, the simulationsassume that the “need” must be fulfilled by some technology. It is entirely conceivable that aresource constraint causes a broader realignment and a reconsideration of what is “needed”and how the need can be alternatively satisfied. In those cases, the dynamics might differ.   17  
    • Discussion, conclusions, and implications for future researchThe motivation driving this research was the paucity of empirical studies on the effects non-financial constraints have on the development of technology and technological change. Ase.g. Gibbert and Scranton (2009) and Hoegl et al. (2010) have already proposed, thesimulations suggest that constraints themselves are largely a neutral force: they are nearlyequally likely to lead to the development and adoption of superior or inferior technologies.They are not very likely to lead to adoption of completely new technologies; in most cases,adopted technologies had existed prior to the constraint. The outcome is surprisinglyresistant to changes in parameter values, remaining even if radical innovation is allowed tobreak the path dependency. These results are in line with research by e.g. Schoenmakers andDuysters (2010), who concluded that radical innovations re-used existing technologies to alarge extent.Nevertheless, the study also shows that constraints can lead to development and adoption ofsuperior technologies. The dichotomous nature of previous work on resource slack on theone hand (e.g. Amabile, 1996; Ancona, 1990; Camison-Zornoza et al. 2004; Damanpour, 1991;Gassman and von Zedtwitz, 2003; Nohria and Gulati, 1996) and the “breaking the barriers”on the other (e.g. Arthur, 2009; Gibbert and Scranton, 2009; Gibbert and Välikangas, 2004;Hoegl et al., 2008, 2010; Katila and Shane, 2005; Mone et al., 1998; Nonaka and Kenney, 1991;Srinivas and Sutz, 2005; Välikangas and Gibbert, 2005; Weiss et al., 2011) on the nature ofconstraints could thus be explained: both views – that constraints impoverish and that theyfacilitate – seem to be correct and can be justified.However, the case may be that constraints do not tend – as a rule – to produce significanttechnological breakthroughs, and research based on “success stories” may be somewhatbiased. If the simplified dynamics used in these simulations hold even approximately, thispaper could explain why there are only a few success stories whose origin can be traced to aconstraint. All other things being equal, instead of spurring new technological advances,constraints seem to play a more limited role by forcing companies to adopt newtechnologies. Previous research (e.g. Popp, 2006; Popp et al., 2011) has concluded thatregulatory constraints are the primary driver of innovation. A more nuanced reading mightbe that constraints are an important driver for adoption of innovations, not so much fordeveloping them. A sudden need that makes existing technologies perform poorly at thetask – a constraint – directs the search more towards existing instead of novel solutions.In particular, firms operating in intensely competitive environments may be faced with aneed to quickly find a satisfactory technology, by copying or otherwise, or exit the market.The end result for this intense competition may be that variety of technologies used willdecrease, better technologies cannot be developed, and industries become more vulnerableto future constraints, as a “monoculture” of dominant designs takes over the industry1.These results are of interest to the evolution of technology research stream. Previousresearch (e.g. Dosi, 1982; Kaplan and Tripsas, 2008) does not usually distinguish betweendifferent kinds of discontinuities. The results presented suggest that the effects of differentexogenous discontinuities may not be symmetrical, and further research towards pryingopen the impact different discontinuities have might be worthwhile.                                                                                                                1 Although constraints were introduced only once for each simulation, the difference between“before” and “after” states in simulations with high imitation intensity is only that the variety oftechnologies in use has decreased even further. Should such an “industry” suffer another constraint,it is very likely that its performance would decline even more.   18  
    • Overall, the research adds to the literature on innovation and technology adoption byproviding a plausible explanation for the conflicting views on the role of constraints in theevolution of technology, and point towards further, empirical research to validate thepatterns detected in the simulations. For example, are technological advances discontinuitiesthat explicitly improve the performance of technologies, or are the dynamics more complexthan that? Future research might also try to establish whether a success bias exists inprevious research on constrained innovation, and to what extent constraints act as driversfor adoption of previously developed technologies. Further research would also bebeneficial to verify the conclusions of this study and to identify factors that influencewhether constraint impoverish or facilitate.In addition to theoretical interest, the results presented here have at least two implicationsfor public policy. First, as Popp et al. (2011) point out, innovation and efficiency are not thesame things. As this paper illustrates, although constraints such as changes in regulatoryregime may very well drive development and adoption of new technologies, there is noguarantee that they will also drive improvements in technology. Policy-makers and interestgroups in many countries have claimed that tightening regulation – for example, CO2emission standards – will drive innovation, and hence increase prosperity while decreasingharmful side effects. While this is a possibility, our findings warn against overreliance on thenumber of innovations or patents as a measure of efficacy for such schemes. Tighteningregulations will probably spur the development and, in particular, adoption of newtechnologies, but the net impact cannot be quantified simply as a number of patents or withsome similar measure. A need for further research on better indicators is obvious.Second, this paper highlights the prevalence of adopting existing technologies as a responseto constraints; as argued above, many “constraint-induced innovations” can in reality beadaptations of already existing technologies. The role of constraint would then be toactualize the need for these technologies or provide a stimulus for actually funding them.Once investments are made and the industry has converged on a single dominant design,breaking the path dependence will be difficult. An important implication would be thatconstraints such as energy and material shortages or regulatory changes will be “come asyou are” crises: there will be little opportunity to wait for developing technologies, asindividual users will adopt (and be locked into) best available existing technologies –whatever those may be. If true, the likely effects of such shortages could be largely assessedin advance. Or, as science fiction author William Gibson put it succinctly, “the future isalready here – it’s just not very evenly distributed.”AcknowledgementsThis research was supported in part by generous grants from the Foundation for EconomicEducation and Jenny and Antti Wihuri Foundation. The authors would like to thankprofessor Liisa Välikangas, professor Jaakko Aspara, Matthias Weiss and Matthew R.Karlsen for their valuable comments and suggestions.   19  
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