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Influences on standards adoption in de facto standardization

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Abstract

In the IT industry, de facto standards emerge from standards competition as firms offer incompatible technologies, and user choices determine the outcome of the competition. The standards literature suggests that strong network effects create a bias toward a standard with a large installed base, leading to a winner-take-all outcome. More recently, several researchers have revealed that the dynamics of standardization are much more complex than the explanation offered by the economic theory of networks. Markets do not always exhibit tipping behavior so there is not always a single winner in de facto standardization; and the size of an overall installed base does not always exert a strong influence on adoption decisions. In contrast, network effects drawn from local social influence may be more salient to user adoption decisions. We ask: (1) Do we always observe a winner-take-all outcome in de facto standards competition? (2) What are the different technology adoption patterns observed in de facto standards competition? (3) What are the implications of network effects, switching costs, pricing, and functionality enhancement strategies on the outcome of de facto standards competition in different user network structures? Drawing on the economic theory of networks, the complex network theory, and previous work in the standards literature, we examine the influence of network effects, switching costs, price, and technology functionality on user adoption decisions using agent-based simulation. We incorporate underlying user network structures frequently observed in the real world as an important determining factor of user adoption decisions. Our results suggest that de facto standardization process does not always follow a three-phased S-shaped pattern. Winner-take-all is not a necessary outcome of standards competition. User network structures have a significant impact on the dynamics and outcomes of standards competition.

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Notes

  1. The literature has yet to develop a consensus on definitions of terms and the appropriate use of various concepts to refer to different aspects of standards and standardization. For example, researchers use numerous terms, such as a standards war [6, 87], standard diffusion [103], and competition between standards [45], to refer to a process in which incompatible technologies compete in a market to become a de facto standard.

  2. See Gallagher and West [47, Table 1, p. 132] for a list of de facto standards competition from 1980 to 1999 and Farrell and Klemperer [40, Section 3] for a brief summary of several well known de facto standards competitions.

  3. Agent-based computation modeling is a useful method to study complex adaptive systems. A complex adaptive system (CAS) is a dynamic network of multiple agents (which may represent cells, species, individuals, firms and even nations) acting in parallel, constantly acting and reacting to what other agents are doing [56]. This kind of systems is self-organized with emergent behaviors. These characteristics of complex adaptive systems are captured using the concepts of complexity, adaptive, or chaos [71, 98]. Holland and Miller [57] suggested that a system can be classified as a complex adaptive system if it satisfies five conditions: (1) a system consists of a network of interacting agents; (2) it exhibits a dynamic, aggregate behavior that emerges from individual activities; (3) its aggregate behavior can be described without a detailed knowledge of the behavior of individuals; (4) the actions of agents in its environment can be assigned a value (e.g., utility, payoff, fitness); and (5) agents have goals to increase their values over time. In this study, we view de facto standards competition as a complex adaptive system for three reasons. De facto standardization is an environment in which a set of various agents interacts. In our case, these agents include technology vendors and users. Agents act and react in response to perceived information about other agents’ behaviors. Some of these behaviors are rivals’ product prices, adoption decisions of friends, and new product functionalities that match users’ needs. An outcome of de facto standardization is much less likely to be determined by a single agent’s actions. In contrast, the outcome is explained by behaviors of all agents in a market.

  4. We extended the Cobb-Douglas utility function that Adner and Levinthal [2] used to study the interaction of technology functionality and price on user adoption decisions. Our model differs from their model in two ways: (1) we added the influence of network effects as another variable that influences user adoption decisions; and (2) we considered both price and switching costs in adoption costs. The value of cost jt was normalized to be between 0 and 1 to ensure that its value is on the same scale as network effects and functionality.

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Authors and Affiliations

  1. College of Information Sciences and Technology, Pennsylvania State University, University Park, PA, 16802, USA

    Angsana A. Techatassanasoontorn & Shuguang Suo

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  1. Angsana A. Techatassanasoontorn
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Correspondence to Angsana A. Techatassanasoontorn.

Appendices

Appendix 1: research activities: using simulation methods for developing theory

We applied Davis et al.’s [35] roadmap for developing theories with simulations in our research to offer a better understanding of the de facto standardization process and outcomes. Our step-by-step activities are summarized in Table 10.

Table 10 Research activities
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Appendix 2: sensitivity analysis results

Similar to robustness checks in regression models, we performed sensitivity analysis to assess the reliability of our results and to obtain additional insights on standards adoption. Sensitivity analysis is also reported in previous IS studies that use computational models [9, 60, 82]. We repeated our analysis by varying four key parameters in our model. These parameters are the population size of users (N), the learning interval (t), the rewiring probability in static and dynamic small-world networks, and the average degree of networks. We varied each of these parameters one at a time while keeping all other parameters exactly the same as in the baseline model. See Table 4 for the values used in sensitivity analysis. We will focus on novel findings from the sensitivity analysis in the discussion below.

2.1 Sensitivity to population size (N)

We repeated the analysis by changing the value of N from 300 to 200 and 400. Table 11 presents the results of sensitivity to N. The values of Turn_inst, Turn_time, Maturity_time, and Turn_Maturity_dur do not significantly change when we varied the population size, suggesting that our results are robust. We noticed that the probability of a winner-take-all outcome (Win100%) may decrease as N becomes bigger. We argue that this outcome can be explained by topological changes in user networks. Table 12 indicates that the characteristic path length increases and the clustering coefficient decreases when N increases. A higher characteristic path length suggests that information sharing may not be effectively diffused among users. A smaller clustering coefficient suggests that users are not highly clustered. Both factors may lead to a failure to build a strong influence through network effects. As a result, a market with a static small-world network structure is less likely to converge to one technology when the population size is large, as evidenced by a higher frequency of pattern B in both static small-world networks and scale-free networks when N = 400.

Table 11 Summary statistics for sensitivity to N
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Table 12 Topological statistics of user networks
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Also, a longer characteristic path length means that information travel slower within a network while a higher clustering coefficient allows user adoption decisions to converge faster. These two counteracting factors jointly shape the results of Turn_time and Maturity_time.

2.2 Sensitivity to learning interval (t)

We varied the learning interval (t) from 20 to 25 and 30. Table 13 reports the results of our analysis. As expected, Turn_time appears to increase as the learning interval gets higher. This suggests that the sooner the users obtain complete knowledge about the technology, the faster the diffusion process becomes. We also observed that an installed base at turning points does not change much with different learning intervals. Also, varying the learning intervals has little impact on Win50%, Win75%, Win100%, and the observed frequencies of each pattern in dynamic small-world and scale-free networks.

Table 13 Summary statistics for sensitivity to learning intervals
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2.3 Sensitivity to rewiring probability

Table 14 shows the results from varying the rewiring probability in static and dynamic small-world networks. Generally, we obtained relatively stable results across different rewiring probabilities. We note that Turn_inst is negatively correlated with rewiring probabilities. The level of installed base at a turning point can be viewed as a minimum threshold to generate enough momentum to support movement toward a dominant technology. Higher rewiring probabilities increase the chance of interactions and thereby facilitate information diffusion between local neighborhoods and the global market. Because information about adoption decisions can be channeled more effectively, it is reasonable to assume that a relatively smaller number of users are required to build the same level of momentum.

Table 14 Summary statistics for sensitivity to rewiring probability
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Another surprising finding is the significant change of observed frequencies of pattern B and pattern C in static small-world networks. As we increased the rewiring probability, we observed fewer occurrences of pattern B and more of pattern C. Again, these results are influenced by the change in topological attributes from changing the rewiring probability. Table 15 shows a decrease of characteristic path length and an increase of clustering coefficient as we vary the rewiring probability from 0.15 to 0.35. Such changes in topological attributes allow information to diffuse more globally. As a result, users may be subject to stronger pressure to follow the crowd and switching may occur more frequently. This may explain why we observe a higher occurrence of pattern C.

Table 15 Topological statistics of small-world networks
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2.4 Sensitivity to degree

We varied the average degree of connection from 4 to 8 and 12 for each network structure. The results are presented in Table 16. As expected, Turn_inst decreases as the degree of connection becomes higher across all three networks. A higher degree of connections allows individual adoption decisions to influence a larger number of other adopters, generating stronger network effects. Maturity_time and Turn_Maturity_dur are significantly higher when the degree is equal to 4 than when the degree is equal to 8 or 12 in statistic small-world networks. As indicated in Table 17, the characteristic path length is substantially longer when the degree is very low, which suggests that it takes a long time for information about adoption decisions to be fully diffused within the network.

Table 16 Summary statistics for sensitivity to degree
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Table 17 Topological statistics of user networks
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An increase in the frequency of pattern A and a decrease in the frequency of pattern B are observed in scale-free networks when we varied the degree from 4 to 8 and 12. This suggests that a higher degree increases the chance that the leading technology can take over the entire market.

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Techatassanasoontorn, A.A., Suo, S. Influences on standards adoption in de facto standardization. Inf Technol Manag 12, 357–385 (2011). https://doi.org/10.1007/s10799-011-0089-2

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