Given a sequence database and minimum support threshold, the task of sequential pattern mining is to discover the complete set of sequential patterns in databases. From the discovered sequential patterns, we can know what items are frequently brought together and in what order they appear. However, they cannot tell us the time gaps between successive items in patterns. Accordingly, Chen et al. have proposed a generalization of sequential patterns, called time-interval sequential patterns, which reveals not only the order of items, but also the time intervals between successive items. An example of time-interval sequential pattern has a form like (A, I2, B, I1, C), meaning that we buy A first, then after an interval of I2 we buy B, and finally after an interval of I1 we buy C, where I2 and I1 are predetermined time ranges. Although this new type of pattern can alleviate the above concern, it causes the sharp boundary problem. That is, when a time interval is near the boundary of two predetermined time ranges, we either ignore or overemphasize it. Therefore, this paper uses the concept of fuzzy sets to extend the original research so that fuzzy time-interval sequential patterns are discovered from databases. Two efficient algorithms, the fuzzy time interval (FTI)-Apriori algorithm and the FTI-PrefixSpan algorithm, are developed for mining fuzzy time-interval sequential patterns. In our simulation results, we find that the second algorithm outperforms the first one, not only in computing time but also in scalability with respect to various parameters.
Purpose
As the application of gamification is gaining great attention and has grown increasingly, thousands of these applications can be easily obtained from mobile phone stores, thus causing intensified competition and discontinuance of use accordingly. Besides, though understanding what factors influence the discontinuance of use of information systems (ISs) is critical for theoretical as well as practical reasons, studies pertaining to the saliency of the final phase, termination of an IS, are still limited. As such, the purpose of this paper is to propose a holistic view to fulfill the above-mentioned research gaps based on the expectation-confirmation model with other salient factors such regret, habit and gamification app values.
Design/methodology/approach
The context of a fitness gamification app is investigated. A total of 210 valid responses were received, and structural equation modeling was applied for data analysis.
Findings
The findings of this paper are as follows: among all factors influencing discontinuance intention, regret is the strongest, habit is second and gamification is third; among all factors affecting user satisfaction, gamification app value is the strongest, confirmation is second, perceived usefulness (PU) and perceived ease of use are third and regret is the last one; for factors influencing users’ habits, satisfaction is the strongest, following by PU and frequency of prior use; confirmation negatively influences the degree of regret; and confirmation positively influences PU.
Originality/value
This study highlights the important determinants influencing users’ discontinuance intentions in the context of gamification apps by incorporating two overlooked factors, regret and habit. Besides, this study suggests that app designers can not only increase user’s perceived value through external cooperation with other alternatives, but can be through internal enhancement with diverse services development as well.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.