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Battle of positioning: exploring the role of bridges in competitive diffusion

Abstract

While social media facilitate product diffusion, the co-existence of competing products makes the diffusion process complex. This study employs an agent-based model to simulate competitive diffusion on social networks and examines the role of a special network position, network bridges, in influencing the diffusion process. The simulation experiments show that targeting bridges can help the weak product with an initially decreasing diffusion curve to increase its market share. The effect of bridges in competitive diffusion increases with the intensity of market competition. This study also reveals that the effect of bridges is larger when the degree distribution of a network has a lower variation. Overall, bridges can be effective alternatives to network hubs in winning market share. Our analysis based on a large-scale real social network further reveals that bridges enhance the offensive and defensive power of a product. This study offers a systematical exploration of the impact of bridges in competitive diffusion under various conditions and the underlying mechanism. It provides guidance for firms competing in social media regarding whom to target (i.e., bridges vs. hubs) and how effective the targeting strategy is.

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Notes

  1. For consumers choosing product B in period t, the adoption model is symmetric. That is, the consumer is influenced by information from friends who adopt A/B and his or her choice inertia to continue to adopt product B. She has two states to convert to in period t + 1: being loyal to product B or switching to product A.

  2. The decision rule for experienced consumers who adopt product B at time \(t-1\) can be developed in the same way as shown in Eqs. (7)–(11).

  3. As a robustness test, we set three levels of intervention duration. With a long-term strategy, Product W targets bridges and keeps them loyal for 100 periods. With a medium-term strategy, Product W keeps them loyal for 50 periods, and then the selected bridges are free to choose a product afterward. With a short-term strategy, Product W keeps selected bridges loyal for only ten periods, and then they are free to choose a product afterward. Although a longer intervention increases the market share of Product W at the end of diffusion, the conclusion that targeting bridges increases the market share of Product W remains qualitatively unchanged. Our key findings are robust across different duration levels. Therefore, we report only the simulation results of the long-term strategy.

  4. If product A (B) is denoted as Product W, then the bridge node is manipulated to be loyal to product A (B) throughout the diffusion process. The same rule applies to the manipulation of targeting the hub or random node. In practice, if the bridge was not an existing adopter of A (B), acquiring the bridge may incur a cost.

  5. In social networks, betweenness centrality and degree centrality are positively correlated. If we select multiple nodes, say, nodes with the top 10% betweenness or degree centrality as targets, many nodes may belong to both categories. In the network of Fig. 1, three of the top four bridges (75%) are also identified as hubs if we adopt the top 10% rule. A similar proportion of overlapping is also found in real social networks. For example, in the real network in Sect. 5, the correlation between betweenness centrality and degree centrality is 0.72. Therefore, choosing multiple bridges or hubs makes it difficult to determine which node type takes effect. To isolate the cause, we select only one node with a clear hub or bridging role as the target.

  6. The number of consumers for Product W at the end of each simulation t is denoted as \({\mathrm{NO}}_{W\mathrm{hub}}\) and \({\mathrm{NO}}_{W\mathrm{bridge}}\) under the two experimental interventions. The relative strength of targeting the bridge to targeting hub is calculated by \(\left(\frac{{\mathrm{NO}}_{W\mathrm{bridge}}}{{\mathrm{NO}}_{W\mathrm{hub}}}-1\right)*100\%\).

  7. We selected different data periods for analysis. For example, we alternatively chose the period of market saturation as the starting period and chose period 100 or 200 as the ending period. The same analysis procedures were applied. The results remain robust.

  8. We alternatively tagged key bridges as those whose betweenness centrality is one or two standard deviations above the mean. Under these two criteria, we identified 2595 and 1157 nodes as bridges, respectively. Because the results are essentially the same, we report only the results based on the top 4000 bridges.

  9. Influencermarketinghub.com, 8 Influencer Marketing Case Studies with Incredible Results. Online document, https://influencermarketinghub.com/8-influencer-marketing-case-studies-with-incredible-results/. Retrieved on January 10, 2020.

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Correspondence to Yunjie Xu.

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The authors declare the following financial interests or personal relationships which may be considered as potential competing interests: this work was supported by the National Natural Science Foundation of China (Grant #71531006 and #71702103) and the Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning.

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Gu, J., Xu, Y. Battle of positioning: exploring the role of bridges in competitive diffusion. J Comput Soc Sc 5, 319–350 (2022). https://doi.org/10.1007/s42001-021-00127-7

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Keywords

  • Social network
  • Competitive diffusion
  • Bridges
  • Competition intensity
  • Degree distribution
  • Agent-based model