Let $X=\{X_n\}$ be a finite state Markov Chain with the state space $\{0,1,2,\ldots,N\}$ such that $0$ is the single absorbing state and all the rest states are transient. The following is the transition matrix.
$$ P = \left[\begin{matrix} 1 & 0 & \ldots & 0 \\ p_{10} & p_{11} & \ldots & p_{1N} \\ \vdots & \vdots & \ldots & \vdots \\p_{N0} & p_{N1} & \ldots & p_{NN} \end{matrix} \right] $$
Now, let $s_{ij}$; $1\leq{i,j}\leq{N}$ be the expected number of time periods that the Markov chain is in state $j$, given that it starts in state $i$. Then, $$ S:=(s_{ij})_{N\times{N}} =(I-P_T)^{-1} $$ where $$ P_T = \left[\begin{matrix} p_{11} & p_{12} & \ldots & p_{1N}\\ p_{21} & p_{22} & \ldots & p_{2N} \\ \vdots & \vdots & \ldots & \vdots \\p_{N1} & p_{N2} & \ldots & p_{NN} \end{matrix} \right] $$ If we let $f_{ij}$ be the probability that the Markov chain ever makes a transition into state $j$ given that it starts in state $i$, then $$ s_{ij} = (\delta_{ij}+s_{jj})f_{ij} + \delta_{ij}(1-f_{ij}) \tag{$*$} $$ where $\delta_{ij}$ is the Kronecker delta.
Now, my question is just the interpretation of the relation $(*)$. How do we explain relation $(*)$ in layman perspective? Or, how to explain relation $(*)$ to those people who do not know any mathematics?
NOTE: The above calculation can be found in the book "Introduction to Probability Models", by Ross.