In large-scale low-stake assessment such as the Programme for International Student Assessment (PISA), students may skip items (missingness) which are within their ability to complete. The detection and taking care of these noneffortful responses, as a measure of test-taking motivation, is an important issue in modern psychometric models. Traditional approaches based on questionnaires and item response theory may have different limitations. In the present research, we proposed a new way by directly using "participant-own-defined" missing item information (user missingness) in a zero-inflated Poisson model. An empirical study using the PISA 2015 data (eight representative economies in two cultures) and another simulation study were conducted to validate our new approach. Results indicated that our model could successfully capture test-taking motivation. We also found that the Confucian students had lower user missingness irrespective of item positions as compared with their Western counterparts.