# The Extended Information Systems Success Measurement Model: e-Learning Perspective
## 2 Background  
### Taxonomy of Information Systems Success  
As seen throughout the previous research and discussions on IS success, many authors have argued over what a successful IS is. However, although much research has been conducted in this area, a single definition of IS success is still not found [15]. IS success research has approached this issue in various ways. Many studies have been conducted over the last decade and a half seeking to identify factors contributing to IS success. However, the dependent variables in these studies on IS success have yet to be discovered. In recognition of this importance, this paper explores the research that has been conducted involving IS success since the first presentation of the IS success challenge, attempting to synthesize this research into a more coherent body of knowledge. It covers the last ten years and reviews all the empirical studies that have tried to measure some aspects of IS success. Authors [PERSON] and [PERSON] presented the D&M IS Success Model in 1992 [1] and thus opened a new chapter, inviting researchers to test the proposed model in different environments. Before they presented this model, researchers were less concerned with IS success, but after publicizing the D&M model, this area expanded. Ten years after, the same authors presented the Updated D&M IS Success Model, based on the previous decade of testing the initial model [16]. After D&M's traction, the Technology Acceptance Model (TAM) was developed in 2003. [PERSON] presented the importance of factors instigating accepting or not accepting information technology. According to the TAM model's definition, the system's perceived usefulness and ease of use are an individual's two most essential expectations about using information technology. Based on the perceived ease of use and usefulness of the system, users develop an attitude towards use and intention to use, which affect the actual usage of the system. [PERSON] believes that, viewed from the perspective of technology users, the perceived usefulness of a system is the strongest predictor of an individual's intention to use information technology [17]. To respond to the need to investigate further the items that make up the perceived usefulness of the system factor from the TAM model [17], bearing in mind that their effect changes over time as the user becomes more experienced in using the IS, an Extended Model of Technology Acceptance was created (TAM2) [18]. Besides the above-presented models, many theories have also been influential in this domain. The Theory of Planned Behavior [19], Social Cognitive Theory [20], and finally, the Unified Theory of Acceptance and Use of Technology gave additional frames to researchers. Based on a review of the extant literature, authors [3] developed UTAUT as a comprehensive synthesis of prior technology acceptance research. UTAUT compounded the essential factors of eight previous theories and models, opening different angles for observing and measuring IS acceptance and success. As of today, several scientifically confirmed models and theories for measuring IS success exist. However, it is apparent that there needs to be a consensus on the measures of IS success. Through years of testing models in different contexts and environments, researchers have found shortcomings and proposed improvements, and as contributions, have presented new models for measuring IS success. Examples of such models that have been empirically validated are TAM2, TAM3, and UTAUT2, and other variations exist. Table 1 contextualizes the distribution of previous research according to the models used. Most of the research was conducted using the D&M IS Success [1]. Around 50% of the papers were based on this single model. At the same time, 42% of papers proposed extensions of the basic model by introducing new, intermediate items and factors that can influence the success of the IS. Notably, nearly half of the conducted studies proposed a new model that combines items and factors from different models to overcome their limitations.