Ranking cognitive processes for eliminating waste in digital text management

Abstract Natural language digital text management is among the most popular but most neglected digital activities. Many consider that no preparation or guided education is necessary, considering that trial-and-error methods or learning-teaching processes that focus on the user interfaces of connected applications are sufficient to cover the subject. However, ineffective digital text management can waste both human and machine resources. Despite numerous warnings indicating a need for more comprehensive approaches, until recently, no methods of measuring the effectiveness of text management had been identified. We investigate the cognitive processes used in text management. We conducted a series of tests where participants’ activities were recorded, and their modified artifacts were preserved. Using these data, we compared and ranked the approaches used in working with erroneous texts. These qualitative solutions were compared, and a measurement system was developed to convert qualitative data into quantitative data. The Alternative Ranking Technique based on Adaptive Standardized Intervals met our needs, which allowed us to establish an objective ranking system based on cognitive processes functioning during the tests. We found that participants rarely employ proactive problem-solving, resorting instead to reactive solutions when faced with unexpected issues. Another key outcome is that if end-users could identify the root causes of errors, they could learn from these mistakes, ultimately increasing their digital effectiveness. Nevertheless, in troubleshooting, progress is rarely observed. In conclusion, education should prioritize the core principles of digital text management instead of focusing on user interfaces alone. This approach leads end-users to reduce and eliminate waste in handling natural language digital texts.