Telecommunication services have to cope with degradations resulting from the necessary transmission of data. A telecommunication service might thus not always be able to provide the same performance to a user. The resulting variation in perceived quality might affect the user’s satisfaction, attitude, behavior, and also future-use intention towards a telecommunication service. This thesis investigates the formation process of perceived quality across multiple, distinct interactions with one telecommunication service. The formation process of the so-called multi-episodic perceived quality is examined for two different time spans. Here, repeated-use in one session consisting of multiple usage episodes is investigated with an overall duration of up to 45 min. This is complemented by studying the formation process spanning several days. This investigation was conducted by performing empirical experiments under controlled laboratory settings as well as field experiments. These experiments are based upon the Mean Opinion Score (MOS), i. e., the assessment of the perceived quality of an (almost) identical stimulus/condition by multiple observers to derive the judgment of an average observer. The impact of individual user behavior was limited here by defining the task, content, and also time for each usage episode as well as the provided performance (defined-use method). The empirical data shows that applying the defined-use method is feasible and yields consistent results. The results of the experiments show that more recent episodes have a higher impact on the multi-episodic perceived quality (recency effect). A saturation is observed for consecutive degraded episodes, i. e., the multi-episodic judgments remain on the same level above the episodic judgments of degraded episodes. In addition, a duration neglect is observed, i. e., a longer degraded episode does not have a higher negative impact on judgments of multi-episodic perceived quality. With the empirical data, models for the prediction of multi-episodic judgments are evaluated. These models are based on the weighted average of the episodic judgments. The evaluation showed that a linear function outperforms a window function in regard to prediction accuracy and robustness.
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