IMPACT OF SUPER USER SUPPORT ON USER PERCEPTIONS AND SATISFACTION WITH INTEGRATIVE TECHNOLOGIES: A SOCIAL PRESENCE PERSPECTIVE
Why this work is in the frame
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Bibliographic record
Abstract
Enterprise Information Systems (EIS) are large systems that enable the integration of business processes and allow seamless business process data flow throughout the organization. An EIS implementation is considered a failure if it is being cancelled; if it is removed early with relevant financial and organizational losses; or if the implementation resulted in a system being underutilized due to dissatisfaction, overspend or poor requirements gathering. Despite excessive spending over the years on digital transformation projects of such systems, failure rates have been excessively high. This research explores Super User effectiveness as an integral part of digital transformation processes. Super Users are regular but highly motivated employees who receive additional training in the use of a new or upgraded computer system to be introduced in the workplace, so that they can provide first-line technical support and training to their local colleagues. Super Users are frequently engaged in guiding and supporting users throughout and after EIS implementations or system upgrades. User satisfaction with the training process and Super User support effectiveness tends to contribute to more successful system transition and EIS implementation success. However, the role of Super Users in EIS implementations as a first line of education and support for EIS users has been substantially understudied as a potential way of reducing these failure rates. Although several studies have explored desired Super User characteristics in EIS systems implementation and successful organizational digital transformation processes, there has been a lack of attention to user perceptions of integrative systems as a contributing factor to better system utilization and implementation. This research explores Super User effectiveness as an integral part of digital transformation processes. A Theoretical Model was developed that draws from accepted theories of collaborative technology, technology adoption, and expectation confirmation. A survey was used to gather responses of 321 end users about their perceptions of Super User support and effectiveness, derived from their experience in several organizations that had undergone digital transformation. The study data were analyzed quantitatively, and the model validated through a structured equation model that was developed, based on relevant published models. A further explanatory study was conducted through thematic analysis of written participant responses. Our study found that Super User ability to emphasize the collaborative features of integrative systems by augmenting user perceptions of EIS as a social presence medium can contribute to higher levels of user performance and satisfaction. Immediacy of integrative systems as well as Individual user characteristics were found to play a positive role in user performance and satisfaction improvement. Situational characteristics of resource-facilitating conditions was also found to contribute positively to user performance and satisfaction. This study contributes to existing research on integrative systems characteristics and Super User effectiveness. It emphasizes collaborative components of integrative systems and discusses additional tools and expanded capabilities for systems utilization and user learning. It also expands on our understanding of Super User effectiveness through an exploration of user perceptions of integrative systems as a social presence medium and effective collaboration tool. Practitioners can thereby emphasize to users the resulting augmented capabilities that can contribute to effectiveness of the Super User training and development process. Practitioners should therefore urge organizations to focus on Super User selection and development as effective organizational resources that facilitate user support through organizational changes associated with EIS implementations, thereby contributing to increases in EIS implementation success rates.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.017 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it