Testing Utaut on the Use of Erp Systems by Middle Managers and End-Users of Medium- to Large-Sized Canadian Enterprises
Notice bibliographique
Résumé
ABSTRACT Individual acceptance and use of new technologies has been studied extensively over the last two decades. And, as more and more organizations move from functional to process-based information technology (IT) infrastructure and enterprise resource planning (ERP) systems are becoming one of today's most widespread IT solutions to this movement, the research literature on ERP systems has exponentially grown. Effectively, the importance of the ERP industry to the professional information systems (IS) community is further underscored by projections indicating that it will be a $47.7 billion industry by 2011 (Jacobson et al, 2007). To study acceptance and use of ERP systems by enterprises and their employees, several models of technology adoption are used, including the Technology Acceptance Model (TAM) (Davis, 1989), its successor the TAM2 (Venkatesh & Davis, 2000), a combination of TAM2 and the model of determinants of perceived ease of use, that is TAM3 (Venkatesh & Bala, 2008), as well as the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2003). But, acceptance and use of ERP systems has not been yet studied in medium- to large-sized Canadian enterprises. The aim of this study is to fill this gap. Using UTAUT model, we gathered data from middle managers and end-users in six medium to large-sized enterprises from three Canadian regions to identify influencing factors on the use of ERP systems. Data analysis was performed using structural equation modeling software, Partial Least Squares (PLS). The results highlight the key role of three independent variables (facilitating conditions, anxiety, and behavioral intention) and a moderator variable (age) of UTAUT model as influencing factors on the use of ERP systems in medium- to large-sized Canadian enterprises. The independent variable social influence can also play a less significant role (p INTRODUCTION It is now evident that information systems (IS) are used at all organizational levels to manage all activities of the enterprises, as much small- to medium-sized enterprises (SME) as medium- to large-sized enterprises. Further, since more than a decade, enterprise-wide IS has gradually been adopted by these two types of enterprises. Indeed, it stands that one of the most pervasive organizational change activities in the last decade or so has been the implementation of enterprise-wide information technologies (IT), such as enterprise resource planning (ERP) systems, that account for 30 percent of all major change activities in organizations today (Davenport, 2000; Herold et al., 2007; Jarvenpaa & Stoddard, 1998; quoted in Morris & Venkatesh, 2010). Some estimates suggest that ERP adoption is as high as 75 percent among medium- to large-sized manufacturing enterprises (Meta Group, 2004; quoted in Morris & Venkatesh, 2010) and about 8 percent among SMEs (Raymond & Uwizeyemungu, 2007). In their comparative analysis of the factors affecting ERP system adoption between SMEs and large companies, Buonanno et al. (2005) showed that business complexity, as a composed factor, is a weak predictor of ERP adoption, whereas just company size turns out to be a very good one. In other words, according to these authors, enterprises seem not to be disregarding ERP systems as an answer to their business complexity. Unexpectedly, SMEs disregard financial constraints as the main cause for ERP system non-adoption, suggesting structural and organizational reasons as major ones. This pattern is partially different from what was observed in large organizations, argue Buonanno et al. (2005), while the first reason for not adopting an ERP system is organizational. On the other hand, Ranganathan and Brown (2006) found a positive relation between ERP system adoption and a favorable reaction on the part of investors. They also found support that ERP projects with greater functional scope (two or more modules) or greater physical scope (multiple sites) result in positive, higher shareholder returns when implementing an ERP system. …
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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 | 0,004 | 0,001 |
| 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 | 0,002 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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.
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