Table 6 Decision rules and intervention... | Emerald Publishing

Table 6

Decision rules and intervention selection logic

Decision rule	Decision-making approach	Decision rule application for decision-making
Maximax	Optimistic (upside seeking)	For each potential intervention within a digital proficiency tier, list the maximum payoff value across the three states of nature Compare the maximum payoffs of the different intervention options The decision is the potential intervention with the highest value from this list
Maximin	Pessimistic (downside protection)	For each potential intervention within a digital proficiency tier, list the minimum payoff value across the three states of nature Compare the minimum payoffs of the different intervention options The decision is the potential intervention with the highest value from this list
Laplace	Equal-weight average	For each potential intervention, calculate the mean of the payoff values across the three states of nature The decision is the potential intervention with the highest mean payoff value
Hurwicz	Tempered optimism, using the optimism coefficient alpha (α), also known as the coefficient of realism	Decide upon a value for the alpha coefficient (α = 1: pure optimist becomes maximax; α = 0: pure pessimist becomes maximin; a value of 0.5 indicates a neutral stance) For each potential intervention within a digital proficiency tier, calculate a Hurwicz value: Hurwicz value = (α * best payoff) + ((1 − α) * worst payoff) The decision is the potential intervention with the highest Hurwicz value
Minimax regret	Regret-averse	Build a regret table within a digital proficiency tier by first identifying the best payoff value for each state of nature column (across the potential interventions in the level) For each potential intervention in that state, calculate its regret: Regret = (best payoff for that state) - (payoff of the potential intervention) Calculate the regrets for all states of nature across all interventions within the digital proficiency tier Identify the maximum regret value for each potential intervention The decision is the potential intervention that has the lowest of these maximum regret values
Tie-break procedure	Equal potential intervention scores are addressed using a tie-break procedure	Perform a broader alignment assessment by counting or weighing how often each intervention comes out stronger across the remaining rules Select the potential intervention with broader support across the remaining rules

Decision rule	Decision-making approach	Decision rule application for decision-making
Maximax	Optimistic (upside seeking)	For each potential intervention within a digital proficiency tier, list the maximum payoff value across the three states of nature Compare the maximum payoffs of the different intervention options The decision is the potential intervention with the highest value from this list
Maximin	Pessimistic (downside protection)	For each potential intervention within a digital proficiency tier, list the minimum payoff value across the three states of nature Compare the minimum payoffs of the different intervention options The decision is the potential intervention with the highest value from this list
Laplace	Equal-weight average	For each potential intervention, calculate the mean of the payoff values across the three states of nature The decision is the potential intervention with the highest mean payoff value
Hurwicz	Tempered optimism, using the optimism coefficient alpha (α), also known as the coefficient of realism	Decide upon a value for the alpha coefficient (α = 1: pure optimist becomes maximax; α = 0: pure pessimist becomes maximin; a value of 0.5 indicates a neutral stance) For each potential intervention within a digital proficiency tier, calculate a Hurwicz value: Hurwicz value = (α * best payoff) + ((1 − α) * worst payoff) The decision is the potential intervention with the highest Hurwicz value
Minimax regret	Regret-averse	Build a regret table within a digital proficiency tier by first identifying the best payoff value for each state of nature column (across the potential interventions in the level) For each potential intervention in that state, calculate its regret: Regret = (best payoff for that state) - (payoff of the potential intervention) Calculate the regrets for all states of nature across all interventions within the digital proficiency tier Identify the maximum regret value for each potential intervention The decision is the potential intervention that has the lowest of these maximum regret values
Tie-break procedure	Equal potential intervention scores are addressed using a tie-break procedure	Perform a broader alignment assessment by counting or weighing how often each intervention comes out stronger across the remaining rules Select the potential intervention with broader support across the remaining rules