IMPACT OF SUPER USER SUPPORT ON USER PERCEPTIONS AND SATISFACTION WITH INTEGRATIVE TECHNOLOGIES: A SOCIAL PRESENCE PERSPECTIVE
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,017 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».