The Franchise-franchisee Model

Trias has invented a first of its kind franchise staking model, which incentivizes franchisors and franchisees by creating an enterprise products ecosystem and by token appreciation.

The Franchise network is introduced into Trias ecosystem because the market expansion of a blockchain product is similar to a traditional franchise model. Definitions of franchisors and franchisees in the context of blockchain development businesses are:

Franchisors: Organizational and individual developers can utilize Trias infrastructure of chains and smart contracts to develop chains and DApps. Thus, a franchisor can be 1) a public blockchain; 2) a protocol/toolkit affiliated to Trias ecosystem; 3) an enterprise blockchain product/solution.

Franchisees: Local franchisees have special terms with franchisors on the right to sell products and services to enterprise clients in a region. A typical franchisee can be a distributor of software products with established channel and client resources.

Both franchisors and franchisees can generate their own native tokens, use Trias tokens, or still use fiat money.

The network helps to create real value for all participants. The model incentivizes the development and upgrade of a product from a franchisor because it attracts the sellers (franchisees) and later aligns with interests of the franchisees of the product by ensuring them to stake tokens. The number of tokens staked is based on the number of products that can be sold. Therefore, the better the infrastructure and the products, the more trade surplus could be generated, which creates a natural selection of good products. Meanwhile, the upfront stake brings more experienced franchisees, which is a natural selection of distribution channels. The more the products are developed and sold, the larger the economy grows, so will the utility and value of the tokens.

1.A General Description of Staking Process

A franchisee is required to stake tokens as part of its service agreement. A franchisee and franchisor negotiate terms based on the franchise agreement. The negotiations include the tokens required for franchise products and time. This agreement is recorded on the public blockchain. The tokens for stake are locked in a pool by the franchisee based on the agreed terms.

A franchisee can benefit from the sale of the products and gains of the token appreciation during the staking period. The whole staking is a non-inflationary as no new tokens are minted.

In addition to staking, franchisees are required to share a percentage of revenue with franchisor. The franchisor can receive the income (as in the case of individual developers) or reward to its community based on its token economics (as in the case of public chains).

2.The Parameters

A generalized case is built, considering parameters that are standard among all franchise contracts. The followings are parameters identified relevant:

• Contract term $T$,which is a generalized agreement to represent contracts in the franchise network.

• Franchise-to-client sale time and price $(τ_i,P_i^{fc})$ for $i = 1,2,⋯N$,where $N$ is the total number of sale units during the contract period.

• Franchiser-to-franchise price per unit $P_{ff}$, which represents the cost per product from a franchisee perspective.

• Unit quote $n$ in the contract, which is a quota limit on how many products a franchisee can sell. This is correlated with the number of tokens staked by a franchise.

• Token price $S_i$, which is determined from the market or the moving average price of the token.

• Interest rate $r$, which is the opportunity that a franchisee sees while staking the tokens. It is a risk-free savings rate offered by monetary institutions.

The above parameters have been carefully set due to the following considerations:

1) To separate general variables from custom variables.

2) To make sure that general variables are not affected by the special types of franchises.

3) All franchises get the same set of rules.

4) To take into consideration the opportunity cost (Interest rate). The interest rate is the same for all franchisees for simplicity of the model.

5) A franchise will incur basic costs for purchasing the product quote from franchisor.

There are also terms that are set from negotiations. The following variables are set case-by-case:

• Proportion of sales for the franchisee $η$, which determines how much sales go to the franchisees. It helps in determining the profit for franchisees.

• Staking rate $d$ for unit quote of the product ($I = d * P_{ff}$ is the staking value of unit quote).

• Buy-back strike price $K$, which is a put option and helps to secure franchisees from financial shocks. The staking tokens are similar to an upfront investment, but put option encourages franchisees to adopt the model.

Suppose the token price without the franchise contract is:

$d\overset{-}{S_t} = \overset{-}{S_t}(μdt+σdW_t)$

With the franchise contract, the token price is:

Here $\Bbb{Q}$ is the martingale probability measure for a fair pricing. Later, $P(S,K;t)$ is used to denote the price of the put option on the token (when $S_t=t$), which gives the holder of the option the right to sell one unit of the token at time $T$ at price $K$, i.e., the payoff at time $T$ is max⁡ $\lbrace K−S_T,0 \rbrace$

Due to cryptocurrencies’ speculative features, there is little control on the price movement from the speculation. However, as shown in the equation above, the franchise token design has a positive impact on token price. If franchisors use the revenue from the sales of products to purchase back tokens from the market, it increases the demand for tokens, which supports the market price.

3.Franchisee Benefits and Cost Analysis

The followings are the benefits from participating in the franchise network:

• Income from sales:$ηP_i$at time$τ_i$, where $i = 1, 2, · · · N$

• Buy-back profit:$n \max\lbrace S_T , K \rbrace$ at time $T$, which is a financial variable considering the token appreciation from staking over time.

Possible costs to join the franchise network:

• Sales cost: sunk cost −$c_0$ and operational cost −$c_1 dt$. Sunk cost is an upfront fixed cost incurred by franchisees at the beginning. The cost is independent of the sales and it not recoverable once spent. This is the cost that incurs for operation. The more the sales occur, the higher the operations cost.

• Staking cost −$d(n)P^{ff}$at time 0. It is the costs that would incur for staking the tokens. This cost can be redeemed from the buyback process at the end of the franchise contract.

• Financing cost $f(ndP^{ff})$

Here d(n)= n * discount with discount being the discount in the unit price for n unit sale.

With these costs and benefits being identified, a utility function is formed to find the net effects of a franchisee joining the network. To maximize the utility function U:

Here, the outer function reflects franchisees' options to join the franchise network and maximize profits.

The inner function reflects franchisees’ optimal negotiation on the staking token buyback price.

Inside the outer operator, there are two terms that are divided to give the net revenue from platform participation. The first term gives the benefits and the second reflects expected costs.

In the first term, there are three terms related to sales of franchisees and are multiplied by the proportion ratio $η$ with the sunk and operational cost. The opportunity cost (which is the interest rate, if not participating in the network) are also determined.

It is worth emphasizing that in the token-based franchise network. A franchise must stake tokens for at least a contract time $t$ to complete services and gain revenue. This holding period exposes franchisees to the token price change over $dt$(change with respect to time). Since this is mitigated from the buyback option offered by the franchisers. So, the inner function is a source of the utility of the equation.

To determine the customized and negotiable variables:

Firstly, the proportion of sales for the franchisee shows a positive correlation with franchisees use. The more the proportion ratio, the more a franchisee can gain from a single product trade.

Secondly, the staking rate for a unit quote of the product is inversely proportional to the utility. Thus, franchisees prefer staking fewer tokens for each product.

At last, the buyback price is within the inner operator, that means the higher the buyback price, the more comfortable a franchisee works in the token-based staking platform.

Token price is positively correlated to the expectations of future productivity growth and user adoption rate. Therefore in a well-functioned franchise network, franchisees enjoy not only the trade surplus but also the investment return from the token price appreciation.

4.Franchisor Benefits and Cost Analysis

The franchisor needs to estimate the benefits and costs of launching a franchise network. A franchisor is the designer of the network and needs to make sure that the profit margins are optimistic and is attractive to the franchisees.

🔹 The followings are the benefits：

• Staking income $d(n)P^{ff}$: the franchisor benefits from income earned from tokens staked by franchisees.

• Holder benefits on token: $\frac{m}{M}(1-η)P_i^{fc}$at time $τ_i$. A Franchisor receives revenues in the form of tokens from the sales of products. The more the sales, the higher the rewards received. The benefits are calculated by the real time price of the token.

• Token promotion benefits: $v(n(d)P^{ff})$, where $v$ is an increasing function, describing the compensate of not getting the promotion effect. The benefits come from the attractiveness of the franchisor’s token. The franchise network increases the utilities of the token and providers cushions periodically, which increase intrinsic value of the token and attract more token holders.

🔹 The following is the cost:

• Buy-back cost −$n \max\lbrace S_T , K \rbrace$at time $T$, which is main cost that the franchisor needs to consider since the franchisor takes responsibility and risk to the tokens staked by the franchisees. If the buy-back price is higher than the market price at the time of buy back, the franchisor bears the difference.

Franchisors try to maximize their benefits, so a max function is created to describe the utility:

where $n$ is the optimal unit in the franchisee’s problem, which depends on $η,K$ ,and $P(S_0;K,T)$ is the price of a put option with strike $K$ and expiry $T$. In the function, two terms are important to determine the net benefit. Firstly, the greater the term $1-η$, the more benefit franchisor will get. This means that the franchisor wants to have a greater proportion of the revenue from each sale of franchisees. Secondly, $(S_0;K,T)$ is inversely proportional to the benefits. The lower the price of a put option and the shorter the expiration time, the more benefit a franchisor will get. As franchisors launch the platform and create tokens, the appreciation of tokens and the growth of the community through franchise network benefits franchisors.

5.Trias Example in the Franchise Model

Currently Trias has incubated TriasForce, a complex of DSaaS products for enterprise clients, as one of the MagCarta applications. Trias Alliance has formed and developers and service providers are joining from different geographic regions. In this case, Trias acts as franchisor and calls for franchisees. For each franchise, a staking as an upfront will be created.