5.3.2 Shaping the Transaction Profile: A Three-pronged Approach

The transaction profile of a platform isn't solely a reflection of its user count or its marketed potential (Clegg et al. 2009). It's a nuanced picture painted by myriad factors, each contributing uniquely to the overall transactional behavior. Our model distills this complexity into three primary components (Shi et al. 2021), each capturing a pivotal aspect of the transaction landscape:

• Emerging Effect (Rogers, Singhal, and Quinlan 2019):

  o Description: This component captures the initial fervor surrounding a new platform. Think of it as the honeymoon phase, where the platform's novelty and distinctive features lead to a surge in transactions. However, like all good things, this phase has expired. As the platform matures and the initial excitement simmers down, the transactional fervor wanes, leading to a decline in transaction numbers.

  o Modeling Approach: The emerging effect is encapsulated using an exponential decay function. Here, stands for the initial transactional burst, denotes the decay rate capturing how quickly the initial enthusiasm diminishes, and models the platform's initial positioning in the market. , a constant, signifies the steady-state transaction rate, the new normal after the initial hype has settled.

$$EmergingEffect(t)=c_e+k_e×exp⁡(-b_e×t)×exp⁡(-l_e×C(-b_e×t))\ \ \ \ \ \ \ \tag{13}$$

• Market Share (Cooper 1993):

  o Description: As the platform carves its niche in the market and gains traction, its transactional activity is influenced by its market share. Essentially, a larger slice of the market pie translates to more transactions.

  o Modeling Approach: The market share's impact on transactions is represented by a logistic function. In this function, is the apex of market share the platform can achieve, characterizes the pace of market share accretion, and is the inflection point, the moment when the market share's growth rate is at its zenith.

$$MarketShare(t)= \frac{B_m}{1+exp⁡(-b_c×(t-t_0 )) } \ \ \ \ \ \ \ \tag{14}$$

• Usefulness:

  o Description: Beyond initial hype and market share, the platform's inherent value proposition, its usability, plays a pivotal role in shaping transactional behavior. A platform that seamlessly addresses user needs and offers tangible value will naturally see more transactions than one that's cumbersome or misaligned with user expectations.

  o Modeling Approach: The usefulness factor is captured by a logistic decay function. In this representation, is the baseline usability level, represents the decline rate in usability, and indicates the shift in the adoption curve.

$$Usefulness(t)=C_u×\frac{1}{1+exp⁡(a_u×(t-k_u )))} \ \ \ \ \ \ \ \tag{15}$$

Combining these components, the overall transaction profile can be expressed as:

$$K_{TX}=EmergingEffect(t)+MarketShare(t)-Usefulness(t) \ \ \ \ \ \ \ \tag{16}$$

As observed, this model closely aligns with Gartner's methodology for analyzing technological hype in the sector (Dedehayir and Steinert 2016). We posit that transaction volumes within a specific network predominantly adhere to similar principles. While a more sophisticated strategy can incorporate deep learning techniques for data analysis, it demands a comprehensive model that considers an array of influential factors (Shi et al. 2021)

From our simulation, the transactional profile of the environment is depicted in the subsequent figure. It's crucial to approach this model with caution as it's grounded in logical assumptions rather than empirical data. This methodology is suitable for initial tokenomics models tailored for nascent networks. In our upcoming iteration, we will juxtapose the constructed model with insights derived from ML algorithms. Nonetheless, it's essential to understand that even when leveraging actual data, every unique scenario will possess distinct factors that significantly shape the transaction profile.

Figure 74. Transaction Profile K_TX over time