Organizations are faced often with multiple problems to solve at any given time, so how do they decide which problems merit attention first? Bilian Ni Sullivan of HKUST explores this problem in the U.S. Federal Aviation Administration, which tackles both human and non-human (such as aircraft) problems and has to come up with new rules to address each problem.

Classic decision-making theories argues that organizations have a limited attention capacity and Sullivan finds favor in this argument (the counter argument says that if attention is allocated with discipline, such as Eastern mindfulness, then there is plenty of capacity). Organizations like the FAA must cope with the many problems competing for attention, a number of which are highly complex in nature and involve multiple participants in reaching a solution - conditions not conducive to mindfulness.

Faced with limited attention capacity, then, they tend to be driven by the urgency of problems over things like their significance when deciding where to put their energies.

"'Urgency' from problems embedded in institutional pressures can push an organization to move faster on solution generation, but this may or may not solve the real problems," Sullivan says.

She finds evidence for this by looking at rules proposed by the FAA in response to human and non-human accidents and incidents from 1983-2000. Each proposed rule has a docket number so she is able to trace the rule from the time of proposal to finalization.

Sullivan finds that when additional problems arise, the likelihood of finalizing rules increase by 4.4 per cent, but this finalization is not significantly related to the importance of the rules, lending weight to the urgency argument.

Also, certain types of problems get more attention depending on other problems that come into play. If there is an increasing number of problems involving human problems, this increases rule finalizing for all human-related problems but decreases that for non-human problems, and vice versa. This supports the idea of limited attention span.

Another factor affecting the generation of solutions is industry performance. Other research has shown organizations tend to be more risk-seeking and likely to change when faced with losses, and more averse to risk when facing gains. Sullivan shows that the positive effect of new problems on rule generation is stronger when the airline industry experiences a low performance level.

"Given the fact more safety problems in airlines often accompany lower industry performance averages, the urgency effect created by new problems can be highlighted because there is an even greater need to assure the public that the agency is working hard to make the public safer when the industry performance level is low," she says.

The final consideration is the complexity of problems. Sullivan shows that under urgency, attention is channeled more to less-complex solutions as indicated by such things as the number of interest groups involved in finalizing a rule.

In sum, she says, "at the stage of proposing rules, problems from different domains [such as human and non-human] compete for attention, with attention paid to the domain with the greatest number of problems. At the stage of rule finalization, the rulemaking rate is impacted by the urgency introduced by new problems."

In the case of the FAA, all new problems tend to push the FAA to finalize more rules, industry performance can significantly moderate the urgency effect, and urgency interacts with the existing priorities given to different types of rules. Overall, the findings highlight the importance of context in assessing allocation to problems and solutions, she says.