The Role of Work Task Complexity in Stopping Information Search

Document Type : Research َ Article

Authors

1 PhD student of Shiraz University, Shiraz, Iran

2 Department of Knowledge and Information Science, Faculty of Education and Psychology, Shiraz University, Shiraz, Iran

10.30484/nastinfo.2025.3627.2286

Abstract

Purpose: This research aimed to determine the role of Work tasks’ complexity levels in stopping information search behavior among graduate students of Shiraz University. This behavior includes session-level stopping and query-level stopping.
Method: This research was practical in terms of its purpose. Regarding the nature of the data, it was quantitative research, and from the perspective of the data collection method, it was descriptive. The study employed quantitative content analysis and structured observation. The statistical population consisted of graduate students at Shiraz University, and the sample size was calculated using JPower software version 3.1.9.7. For this purpose, the appropriate effect size, based on behavioral studies in human-computer interaction, was set at 0.89, with an alpha error of 0.05 and a 95% confidence level. According to the types of tests used, the sample size for the Yeoman-Whitney test was determined to be 72 participants, while for the independent one-sample t-test, it was 68 participants. To ensure greater reliability, data was collected from 80 students; however, the data of three students were excluded due to outliers, resulting in a final analysis of 77 students. Eight tasks were created using the repository of assigned search tasks to conduct the research. After confirming the validity of the tasks with information science professors, four experts in task design assessed the complexity of the tasks using a Likert scale. Finally, based on the experts' agreement, two simple tasks and two complex tasks were chosen. Students used the Google search engine to search tasks while Camtasia software version 2019.0 recorded all their transactions. The recorded user transactions helped identify two types of search stopping during the students' information search session for each task: good stopping (stopping due to finding the answer) and bad stopping (stopping due to not finding the answer). Additionally, the occurrence of query-level stopping (query reformulation) was also recorded in every task. After checking the normality of the data distribution using the Kolmogorov-Smirnov test, the impact of task complexity was determined on session-level stopping and query-level stopping using the Mann-Whitney U test and independent groups't-test in SPSS version 26.
Findings: The results showed that the complexity of tasks affected how often people stopped searching for information. As tasks became more complex, the rate of good stopping decreased, and the occurrence of query-level stopping increased. In other words, when people completed complex tasks, they found the correct answer less often and often stopped searching because they couldn't find the answer. However, compared to simple tasks, people formulated more queries to complete complex tasks, attempting to find a better answer.
Conclusion: The results showed that the complexity of tasks affected how often people stopped searching for information. As tasks became more complex, the rate of good stopping decreased, and the occurrence of query-level stopping increased. In other words, when people completed complex tasks, they found the correct answer less often and often stopped searching because they couldn't find the answer. However, compared to simple tasks, people formulated more queries to complete complex tasks, attempting to find a better answer.

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References

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  • Receive Date: 14 July 2024
  • Revise Date: 05 October 2024
  • Accept Date: 06 November 2024

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