Investigation of continuance stream-watching intention: an empirical study
Information Technology and Management, ISSN: 1573-7667
2024
- 8Captures
Metric Options: Counts1 Year3 YearSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Captures8
- Readers8
Article Description
With the popularity of live streaming, viewers have been found to spend an increasing number of hours on stream-watching in a continuous manner. Why users choose to watch streams for the first time has been well explored. However, continuance stream-watching phenomenon has not been investigated enough. Given the importance of accumulating loyal user base for all live streaming platforms, accumulating users can be equally important to attracting new users. However, understanding in the continuance behavior seems to receive limited investigation in the live streaming context. This study introduced expectation–confirmation theory (ECT) to explain how continuance stream-watching behavior is cultivated. Before, ECT has been extensively applied to explain continuance intention. However, most ECT-based models are not adequate to predict continuance watching intention in the digital context since they only emphasize on users’ perceived benefits and costs of watching but neglect the influence of uses’ sunk costs. Therefore, this paper proposes a new research model in which perceived benefits, perceived sacrifices and perceived sunk costs are included. Our proposed model was empirically validated by an online survey (N = 438). It provides theoretical evidence of antecedents of viewers’ continuance watching intention. This paper advances our understanding in continuance behavior in live streaming. It discusses practical implications and provides strategies to live streaming platforms as well as streamers.
Bibliographic Details
Springer Science and Business Media LLC
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know