Capability-based governance patterns over the product life-cycle: an agent-based model

Contingency models of the industry evolution (e.g. Levitt 1965; Wasson 1974; Utterback and Abernathy 1975) describe the pattern of interlocking evolution of demand, competitive activities, and technology research and development. The evolution of industries with homogeneous demand⁵ is expected to abide by the cycle of technological change (cf. Malerba 2006) of the alternating era of ferment after a breakthrough technology and era of incremental change following the emergence of a dominant design (Anderson and Tushman 1990; Tushman and Anderson 1986). In the era of ferment, entrepreneurs compete on different product-market propositions. In the era of incremental change, firms follow the dominant design and rival for consumers’ demand by price competition through cost reduction, efficiency increases, and process innovation. With a further erosion of profit margins, firms seek to develop the next generation of products with, again, high profit margins. This radical breakthrough innovation triggers a new onset phase.

Various authors (e.g. Klepper 1997; Murmann and Frenken 2006) have stressed that the existing contingency models of industry evolution lack a vertical orientation, i.e. do not account for upstream industry conditions that affect the industry development. Recently, scholars engaged in describing the governance patterns over the product life-cycle (cf. Fine 1998; Jacobides and Winter 2005; Cacciatori and Jacobides 2005; Argyres and Bigelow 2010; Argyres and Zenger 2009). We follow these organizational economists in that both capability-based concerns and cost-based concerns and their dynamic interplay determine the governance form. However, our explicit contention is that during the era of ferment, the emphasis is on R&D such that governance decisions are dominated by capability-based concerns, while, during the era of incremental search, the emphasis is on manufacturing such that governance decisions are dominated by cost-based concerns. From organization ecological perspective (cf. Duysters 1995; Hannan and Freeman 1977) we know that also the type of firms evolves over the industry life-cycle. During the era of ferment, the population consists of many specialized entrepreneurs that compete on product performance and respond swiftly to opportunities, while, in the era of incremental change, the population consists of a few large manufacturing firms that exploit scale advantages of mass-production.

In our simulation model, we adopt the knowledge-based perspective on product innovation (Gilbert et al. 2001, 2007) in which firms look for new capabilities or a new combination of capabilities to produce new, superior products. To realize competitive products, up- and downstream capabilities need to be attuned.

From a theoretical perspective, both governance forms have advantages in searching for a competitive combination of capabilities.

Firstly, there is a synergistic advantage of integration (Schilling 2000). Whenever a firm is vertically integrated, internal communication and cross-fertilization of knowledge is efficient. Input component and system technology can be closely attuned during development such that there is high internal compatibility (cf. Kogut and Zander 1992; Afuah 2001). An additional advantage is that integration excludes rivals from using the same input or system and ensures that the generated value is appropriated by its (integrated) inventor.

Secondly, there is a combinatorial advantage of specialization. Whenever firms are vertically specialized in a modular design, they can link up with alternative complement providers without having to engage in time consuming or costly redesign. For instance, a system producer can switch to an alternative (vertically specialized) input supplier to realize a new combination of capabilities (cf. Schilling and Steensma 2001; Dyer and Singh 1998). In case there are many suppliers that search independently for capabilities, system producers can consider more combinations than when vertically integrated. Furthermore, suppliers may trade with or be approached by more than one assembler, thus increasing their chances of sales. Vertical specialization thus allows decentralized search of many combinations.

So, vertical integration allows attuning for high performance plus appropriation of generated value, while vertical specialization allows recombination with components becoming available due to decentralized search. Note that vertical integration need not be a merger of two firms; a firm can vertically integrate forward or backward by either buying or developing the production capabilities needed.

A firm has a competitive advantage of owning a particular capability if this capability is valuable and rare, and hard to imitate and substitute (Barney 1991). In this study, we focus on imitability and substitutability. Imitability refers to the extent of efforts (costs, dedicated time, or other resources) needed to imitate capabilities. Substitutability refers to the extent to which swapping one capability for another is likely to affect overall performance (conditioned on values of both capabilities). Here, we formulate hypotheses on why firms would vertically specialize or integrate for different levels of substitutability and imitability.

Firstly, if substitutability is high, capabilities are modular, and swapping one component (system) for another component (system) capability is likely to yield few compatibility issues. The more substitutable (more modular/less complex) capabilities, the more eager firms are to specialize (cf. Argyres and Bigelow 2006; Sanchez and Mahoney 1996). Alternatively, if component and system capabilities are complexly interwoven or finely attuned, swapping one for an arbitrary other capability is likely to lead to compatibility problems and thereby deter overall performance. In this case, vertical integration allows attuning upstream and downstream capabilities to arrive at an integrated, competitive product, while specialization and relying on external capabilities is likely to yield products whose performance is low due to incompatibilities. So, with a decrease in substitutability (increase in complexity), the combinatorial advantage of specialization decreases and the synergistic advantage of integration increases. Note that, due to these incompatibilities, integration as exclusion instrument is less important. The lower substitutability, the less valuable combinatorial search, and the less likely firms are to pursue vertical specialization (the more likely firms are to pursue integrated search). We expect this effect to become stronger if imitability drops, because appropriability increases for integrated firms.

Secondly, if imitability is high, the synergistic advantage of being integrated reduces. After all, if capabilities can be imitated relatively easily and relatively cheaply, the additional performance achieved by tight vertical governance vanishes with the swiftly followed imitation. Also the combinatorial advantage of being specialized reduces as (integrated) firms can quickly imitate a high performing combination found by other firms. Conclusively, if imitability is high, both advantages are relatively weak. However, by specializing, firms still enjoy decentralized search and consider more combinations than when fully integrated. Alternatively, if imitability is low, integration is an effective exclusion and appropriation instrument, whereas specialization allows competitors to access the same (possibly superior) upstream capabilities. Note that exclusion is less of a reason to integrate if those capabilities are of limited value for competitors. So, the value of arbitrary complementary capabilities drops whenever the substitutability drops. A system (component) producer does not need to exclude competing system (component) producers from accessing the same component (system) producer as they are likely to encounter compatibility issues anyhow.

Table 1 contains the hypotheses on the governance form sought under different levels of imitability and substitutability respectively as inferred above. In the on-diagonal cells, there is consensus, while, in the off-diagonal cells, there is ambiguity due to conflicting implications.

Recent research is concerned with the temporal patterns in the vertical scope of firms over the product life-cycle (e.g. Fine 1998; Jacobides and Winter 2005; Cacciatori and Jacobides 2005; Argyres and Bigelow 2010; Argyres and Zenger 2009). According to the cycle of technological change (Anderson and Tushman 1990; Tushman and Anderson 1986), the industry alternates between the eras of ferment and incremental change, punctuated by the emerge of a dominant design and a radical breakthrough. In each of these eras, the market, competitive and technological conditions differ, and so are the concerns in deciding on a governance form. Table 1 needs further specification for each of the two eras.

Here we briefly discuss the basic ideas underlying the functional design of and the operationalizations in the simulation model, notably (i) that we seek to reproduce stylized facts of the product life-cycle, (ii) how our model extends the classical PLC, (iii) that product renewal through capability and product search interlocks with diffusion–substitution in consumer demand, (iv) how we seek to ‘evolutionary train’ the governance strategy, and (v) the experimental setup to investigate the effects of capability regime properties on emerging governance patterns.

To study the emerging vertical governance pattern over the product life-cycle, we specify a neo-Schumpeterian model that reproduces the classical PLC in which the industry alternates between two distinct phases of competition, separated by the emergence of a dominant design and a radical breakthrough (Utterback and Abernathy 1975; Anderson and Tushman 1990). In the early phase (era of ferment), the firm population consists of ‘entrepreneurial’ firms that focus on product innovation and run flexible job shops. In the mature phase (era of incremental change), the firm population consists of ‘mass-manufacturing’ firms that operate dedicated, large scale production equipment. Typical simulation results (see Sect. 4) of our neo-Schumpeterian model feature distinct eras of ferment and of incremental change and reproduce the stylized facts of swarm-in/shake-out, turbulence/convergence in product technology, and profit erosion.

However, in our model, we move away from the traditional single tier models of an industry and rather have a two-tier industry. At the downstream tier, system producers/assemblers produce systems to be sold to consumers. At the upstream tier, firms produce components that are input into the system that is made downstream. A vertically integrated firm produces the system and the input component. A vertically specialized system producer buys components from a vertically specialized component producer. Figure 1 illustrates the industry structure.

In any neo-Schumpeterian agent-based model, firms compete for consumer demand through product innovation and price setting. Following the SKIN model (see e.g. Gilbert et al. 2001, 2007), firms look for new capabilities or a new combination of capabilities to produce new, superior products. In Sect. 3.4, we describe how firms with different governance forms search an upstream and a downstream capability landscape for new capabilities and combine these capabilities to make products. A vertically integrated firm explores combinations of the system and component capabilities that it owns, fully in-house. A vertically specialized firm explores combinations of its own capabilities with complementary capabilities owned by potential partners. The capabilities are combined to produce products that are then launched, dynamically priced, and ultimately withdrawn from the market following the heuristics described in Sect. 3.5. In Sect. 3.6, we describe how consumers buy products based on price and performance, their individual willingness-to-buy, and word-of-mouth.

Given that firms incur costs for searching, producing, and changing production, only firms that follow strategies that efficiently generate superior products will survive competition in the long run. The firms in our model follow three different strategies: the business strategy (whether and when to be mass-manufacturer or entrepreneur), the product search strategy (when to explore or rather imitate, when to conduct radical search), and the vertical governance strategy (when to vertically integrate or specialize). In Sect. 3.3, we describe the operationalization of these strategies in detail. The strategies of superior firms are imitated by entrants to establish ‘evolutionary learning’ (at the population level) of these strategies. A flowchart of this industry evolution with evolutionary learning of strategies is given in Fig. 2.

In the present work, firms learn two strategy variables⁶: the governance form (integrated/specialized) before and after the emergence of the dominance design.

The industry level parameters (production costs, diffusion rate, periodic costs, etc.) are treated as mediating variables. The agents’ decision variables (decision thresholds are discussed later) are taken constant and equal for all agents. By homogenizing firms in this way, evolution trains just the governance strategy. The values chosen for mediating variables and strategy decisions parameters are given in “Appendix 1”. In Sect. 4.1.2, we discuss our parameter choices in detail.

Our neo-Schumpeterian agent-based model is a discrete periodic simulation model. In each period, there are four consecutive stages of activities: a sales round, a round of strategic activities (in which firms may change the business model and/or their governance form), a round of operational capability and product search, and an entry and exit round.

Firstly, there is a sales round in which (i) firms select products they will produce and the prices at which they will be sold, and (ii) consumers share experiences and determine what to buy. As described in Sect. 3.5, firms launch new products by giving away promotional units and thus start diffusion–substitution. Moreover, firms remove products that are not consumed anymore or because they are old-fashioned (products of the newest-but-two generation are phased out). Firms set prices of currently produced products by conducting a poll among consumers. As described in Sect. 3.6, consumers consume part of their products, and—if in need of a new unit of product—consult a panel of other consumers (which thus operationalizes word-of-mouth) on which product to buy. Once consumers are aware of the existence of a new generation of products, they become increasingly eager to buy these products. Consumers pick a particular product based on the recommendations of consumers in a panel.

Secondly, as described in Sect. 3.3, firms follow their business, governance, and capability and product search strategies. The capability search strategy is ‘exploration’, ‘imitation’, or ‘radical search’ depending on market concentration, profit margin, and the gap in performance between own and competing products. If a firm is in imitation mode (step-by-step development of the capability of a competitor) or conducting radical search, the firm will finish that before following business or governance strategic actions. If a firm is in exploratory mode, the firm consults its business strategy and may set the business model to ‘entrepreneur’ or ‘mass-manufacturer’ depending on the market share of the product design. If a firm switches from one to another business strategy, it will also immediately vertically integrate or outsource in line with the governance strategy of that new business model.

Thirdly, as described in Sect. 3.4, firms also conduct operational search for new capabilities on a capability landscape. The capabilities found are then combined into new products, either in-house for integrated firms or with another firm upstream or downstream for vertically specialized firms. A vertically specialized firm will also consider all new combinations if a complement producer discovers a new capability. A product thus developed is eligible for production and market entry if (i) the product portfolio does not already contain it, the portfolio is not full or the product’s performance exceeds that of another product currently in the portfolio that it can replace, (ii) an estimate of the willingness-to-pay exceeds the production costs, and (iii) it is the first of a new generation or its performance exceeds that of the current top product on the market.

Fourthly, as described in Sect. 3.7, at the end of each period, firms that are bankrupt do exit (unless it is the last firm in the industry). Moreover, there is a firm waiting outside the industry which decides whether or not to enter.

Each firm follows three strategies: a business strategy, a capability search strategy, and a vertical governance strategy. Following Anderson and Tushman (1990), we have firms change their business model upon the technological punctuations (emergence of dominant design, discovery of a radical breakthrough). Operationally, each agent has its own preferred governance form with either one of the business models. The business strategy, capability landscape search strategy, and the vertical governance are only reconsidered whenever the firm is engaged in exploration. Whenever the firm is engaged in imitation, differentiation, or radical search, the firm first finishes that search operation. We now describe these three strategies in detail.

Following the SKIN model (Gilbert et al. 2001; Pyka et al. 2009), firms conduct a two-stage search. In the first stage, firms search on the capability landscape for new viable capabilities. In the second stage, firms recombine readily discovered component and system capabilities into product propositions, whereby integrated firms do so exclusively in-house, while vertically specialized firms collaborate with firms providing complementary capabilities. We now discuss both search stages.

Each firm active on the consumer market has a portfolio of products that it actually produces and possibly sells on the market. If search yields a new product proposition, the firm evaluates whether it is eligible for production and, if so, adds it to the product portfolio. A product proposition is eligible for production if it outperforms one of the currently produced products or there is still room in the otherwise limited product portfolio. Moreover, the product must have a profit margin that is estimated to be positive. The flowchart for this procedure is found in Fig. 7.

If a firm has not yet produced and sold a particular product before, it has to start up production (for which a fixed cost is incurred) and launch it by giving promotional units to a number of consumers. These consumers will thus (possibly) drive further diffusion–substitution.

A selling firm (i.e. fully integrated or assembler) withdraws a product from the market and removes it from its portfolio if there has been no sales in the current period and there are no potential future sales. Note that also sales by any of the competitors of the same product or consumers currently still consuming the product forms potential future sales.

Firms set the price upon the product launch such that the value-for-money of the product (performance divided by price) is equal to the average value-for-money of products currently on the market. The rationale is that overpricing will stifle diffusion, while dramatic underpricing would hurt revenue too much. In case it is the first product on the market, the price is set to the maximum willingness-to-pay \(p^*\).

Each period, each firm conducts a poll for each of its products to determine whether to change the price or not. In this poll, the firm randomly draws a fixed number B of consumers from the population of consumers that currently consumes any product of the same product generation. The firm then asks each consumer the probability that it would buy the product in case of three scenarios: the price remains the same, is increased a little, and is decreased a little. These probabilities are in fact determined by the discrete choice model discussed later. The firm then maximizes expected sales (average probability times the (new) price) by increasing, decreasing or keeping the current price. Figure 8 contains the flowchart of this process.

With multiple firms and sufficiently discriminative consumers, there is profit erosion and eventually even a (marginal) loss to be expected. Firms will end up losing money, go bankrupt and exit. As demand is inelastic, a remaining monopolist can increase its price indefinitely. As such, we assume that a monopolist will not exit the industry. As we will see in Sect. 3.7, the monopolist will eventually have a high profit margin, which in turn attracts one or multiple entrants that will restore regular price competition.

The temporal pattern in consumer demand for new products is described by the classical Bass (1969) model. In this model, potential adopters of a new product are influenced by both mass media communications and interpersonal word-of-mouth. Mass media communications affect purchase decisions mostly in the early stage of the product life-cycle, while interpersonal communications dominate after this (see Rogers 1995, pp. 79–82). In the model in this paper, consumers that have consumed their unit of product have to buy a new one. In general, consumers have numerous products to choose from, often even products of different generations. Macro-level substitution–diffusion models extending Bass (1969) describe how products of newer generations gradually diffuse in the consumer population and how these products thus substitute the products of old generations (Norton and Bass 1987; Fisher and Pry 1971; Marchetti and Nakicenovic 1979). In these macro-level models consumers ultimately do not differ in their propensities to adopt particular products (or even newer generations of products). In reality, consumers differ in their purchasing power, the willingness-to-pay, exposure to mass media, connections to consumers that may already have purchased a product, etc. Rogers (1995) takes a micro-level perspective on adoption decisions and argues that individual characteristics of consumers in combination with exposure through mass media and word-of-mouth determines the actual adoption propensity. In Rogers’ perspective consumers range from being ‘innovators’ with a high propensity to adopt new products to ‘laggards’ that are reluctant to adopt. Rogers’ perspective also allows modeling of micro-level phenomena such as Veblen’s conspicuous consumption and bandwagon dynamics (cf. Leibenstein 1950).

To reproduce the demand dynamics in the product life-cycle, the demand model in our neo-Schumpeterian model features an initial ‘mass launch’ for each product followed by a relatively autonomous substitution–diffusion due to ‘word-of-mouth’ of heterogeneous consumers. Figure 9 contains the flowchart of how a focal consumer decides when to purchase what product of which generation. The mechanisms and variables in this flowchart are explained in the following subsections.

A particularly distinct element of neo-Schumpeterian models is population-level learning by (i) deselection of evolutionary unfavorable strategies due to exit of firms and (ii) active propagation of apparently favorable strategies due to entry of firms imitating strategies of existing firms.

Upon the initialization of the first run of an industry, there is a fixed number of firms that immediately enters. To prevent favoring a particular strategy, we initialize A firms with all A combinations of integration and outsourcing before and after the emergence of the dominant design. From that moment onward, firms enter and exit the industry fully endogenously. A firm exits the industry if its capital drops below the bankruptcy level (zero). However, we assume that a monopolist succeeds in persuading creditors not to file for bankruptcy. Given the inelasticity of demand, the monopolist can raise its prices (and the pricing heuristic depicted in Fig. 8 in Sect. 3.5 ensures it does), and with the increase in profit margin, entry becomes attractive for potential entrants again. Once a new firm enters the industry, the industry no longer is a monopoly, and the old exit conditions are restored.

Following Nelson and Winter (1982), we assume that there is always a firm waiting outside the industry for the industry conditions to meet its entry criteria to then enter. Each period, the potential entrant (imperfectly) imitates the strategy of the firm with the highest discounted cumulative capital. Upon imitation, the potential entrant changes the governance strategy in 5% of the cases. This constitutes local search in the governance strategy. Following this strategy, the potential entrant determines whether it becomes entrepreneur or manufacturer and whether it would then enter or not according to the criteria on profit margin and design dominance given below.

We follow the characterizations of manufacturing firms over the product life-cycle of Utterback and Abernathy (1975) and Klepper (1996). In the early phase of the PLC, the entrants are small, flexible, and innovative firms (‘entrepreneurs’) that tolerate high uncertainty to reap high profit margins. In the growth and mature phases of the PLC, the entrants are big, capital intensive production houses (‘manufacturers’) that reap low profit margin with dedicated production equipment under low uncertainty. Operationally, we assume that entrepreneurs are willing to enter when (i) the profit margin for the newest generation of products is high, (ii) the design dominance in that generation is low, and (iii) there are not too many competitors. In this case, there are considerable market and technological opportunities. We assume that a manufacturer enters when (i) the profit margin (for the latest generation of products) is not too low, (ii) the design dominance in that generation is already relatively high (‘limited technological uncertainty’), and (iii) there are only a few competitors. In this case, the manufacturer can recover investments while it is unlikely that a switch in production is required. The entry criteria revolve around the profit margin and design dominance. For the design dominance statistic, we follow many neo-Schumpeterian models with entry and use a concentration index for generation g:where \(s_i\) with \(i \in M_g\) is the market share of the ith product of generation g on the market (possibly offered by more than one firm), \(M_g\) is the index set of products of generation g currently on the market, and S is a normalization constant⁹ (over all generations). We compute this H for each generation g of products on the market. Given the volatility of \(s_i\), particularly in the onset phase, we have firms use an exponentially smoothened \(\hat{H} \leftarrow h H + (1 - h) \hat{H}\) with smoothing rate \(h = 1\%\). For the profit margin per generation, we use the weighted average of the profit margins of all products of that generation. We again have firms use an exponentially smoothened average profit margin \(\hat{P}\) as there is volatility due to introduction and withdrawal of products. Given the different tolerances for number of competitors, the entry rates for entrepreneurs and mass-manufacturers differ. Entrepreneurs enter with probability \(e(x) = 1/x\) (with x the number of firms) if \(\hat{P} > P^e\) and \(\hat{H} < H^e\). Manufacturers enter with probability \(e(x) = a/(1 + x)\) with \(0< a < 1\) if \(\hat{P} > P^m\) and \(\hat{H} > H^m\).

We treat the cost structure as a mediating variable. We pick a cost structure that closely meets the classical product life-cycle notions of entrepreneurial activities before and mass-manufacturing activities after the emergence of the dominant design, see Utterback and Abernathy (1975). We assume that an entrepreneur runs a job shop, which is a flexible production facility that facilitates switching to making another product at relatively low costs. However, production in a job shop relies on generic tools rather then specialized equipment, is labor intensive, and requires relatively skilled and thus expensive employees. Moreover, the batch size is relatively small such that there are limited positive scale economies. All in all, job shops have high unit production costs. In contrast, a mass-manufacturer has dedicated production equipment, a relatively low number of employees to run the plant, and thus comparatively low labor costs. A mass-manufacturer produces big batches to reap positive scale economies and thus has low unit production costs. However, given the dedicated production equipment, there are high production switching costs.

Moreover, the vertical governance form affects the immediate production costs. Vertically specialized system producers incur transaction costs to contact, contract, and control suppliers as opposed to in-house production. Firms also incur costs when changing the governance form. Integration requires investments in production facilities, while these facilities are scrapped or divested upon outsourcing. In order to ensure that firms are quickly acquiring an evolutionary beneficial governance strategy during simulation runs, we reduce noise in firms’ financial performance caused by cost particularities by assuming that firms in each of the tiers face the same cost structure. To prevent further escalation in the number of parameters, we assume that the manufacturing costs in the up- and downstream tiers are equal and only depend on whether the firm runs a job shop or dedicated mass-manufacturing equipment. However, we do assume that the system producers in the downstream tier incur the transaction costs for purchasing components from the vertically specialized component producers in the upstream tier.

Apart from these periodic manufacturing costs, there are costs of incremental research, imitation, and radical research. We assume that costs for incremental research and imitation are negligible relative to costs of radical research. The costs for radical research are set so high that any firm can only produce a few commercially unsuccessful radical products before going bankrupt.

The parameter values used in the standard simulation runs are provided in “Appendix 1”. In simulation experiments, we have validated mediating effects of these costs on the governance forms emerging, e.g. if governance change becomes extremely expensive, the emerging strategy is to stick with the current governance form. For an elaborate discussion on the parameter choices, see Sect. 4.1.

In this subsection, we cross-validate the simulation results with common stylized facts in evolutionary economics for industry evolution under homogeneous demand. We start off with a discussion of the parameter values chosen. We then establish that our simulation model of the product life-cycle indeed produces results that feature consecutive product life-cycles with eras of ferment and incremental change with the common stylized facts. We then discuss the effects of our parameter choices on the stylized life-cycles.

We report the findings of extensive simulation runs for four combinations of imitability (low \(i = 0.025\)/high \(i = 0.125\)) and complexity (low \(x=0\)/high \(x=8\)) and provide statistical analysis of the governance patterns emerging. For each of these four combinations, we simulate the industry evolution for 50,000 periods, and for 100 different seed values. For each seed value, we take the governance strategy with the highest (discounted) capital stock at the end of these 50,000 periods and register the governance form in both the eras of ferment and incremental change. We thus construct a \(2 \times 2\) contingency table with the governance form in the era of incremental change on the X-axis and the governance form in the era of ferment on the Y-axis. Figure 11 contains a \(2 \times 2\) table with the four scenarios for the particular settings for imitability and complexity, with the contingency table of the simulation results in each cell of this table. We first discuss the statistics used and then the simulation outcomes, their statistical analysis, and the interpretation thereof.