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Enregistrement W4253434259 · doi:10.1111/1540-5885.1820065

Intermediating technologies and multi‐group adoption: A comparison of consumer and merchant adoption intentions toward a new electronic payment system

2001· article· en· W4253434259 sur OpenAlex

Pourquoi ce travail est dans la base

Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.

affAu moins un auteur déclare une institution canadienne dans l'instantané OpenAlex épinglé.

Notice bibliographique

RevueJournal of Product Innovation Management · 2001
Typearticle
Langueen
DomaineDecision Sciences
ThématiqueTechnology Adoption and User Behaviour
Établissements canadiensWestern University
Organismes subventionnairesnon disponible
Mots-clésBusinessPaymentMarketingContext (archaeology)Order (exchange)Smart cardPayment cardComputer scienceComputer securityFinance

Résumé

récupéré en direct d'OpenAlex

Traditional technology adoption research has assumed a single adopting group. However, there are many settings in which multiple groups must jointly adopt an innovation in order for it to succeed. This is particularly true for new information technology innovations that mediate the relationship between two groups. For example, online exchanges (e.g., Freemarkets, GoFish) must attract both suppliers and buyers in order to be successful. The same is true for providers of hardware/software solutions for electronic data interchange and supply chain management. This article describes the phenomenon of multigroup adoption with a particular focus on applications within the financial services and retailing industries. Empirically, the article reports findings from a study that illustrates the importance of evaluating and managing multigroup technology adoption in the specific context of an in‐market trial of a new smart card‐based electronic payment system. Two distinct groups critical to the smart card's success are studied: consumers (who must decide to use the new card) and retailers (who must agree to adopt and use new technology needed to process smart card transactions). The study identifies which characteristics of the smart card innovation are most closely linked to intention to adopt for each group, and examines how these key characteristics differ by group. Perceptual data were collected via a mail survey from consumers and merchants living in the city where a one‐year market trial of the new card was taking place. Four separate sampling frames were established for both consumers and merchants who were participating in the trial as well as both consumers and merchants who were not participating in the trial. Random samples were then drawn from these frames. More than 350 consumers and over 250 merchants completed and returned the survey. Responses were analyzed separately for each of the four groups sampled. The most important characteristic leading to adoption identified by all four groups was relative advantage—the smart card had to demonstrate a clear competitive advantage over what they currently used. Compatibility (i.e., the degree to which the smart card fit with their current preferences) was also noted as important to all but the nonparticipating merchant group. Beyond this, the key drivers of adoption differed considerably by group. Participating consumers and participating merchants appeared to possess different perspectives when assessing their decision to adopt the smart card technology. Consumers seemed to value the notion that the adoption decision is under their control, whereas merchants seemed to place more value on the antecedents that had the potential to add to their bottom line. This suggests that it is necessary to institute different marketing tactics to attract the early adopting groups. In addition, significant differences in the importance of antecedents between participating and nonparticipating consumers and participating and nonparticipating merchants suggest that, over time, it may also be necessary to develop and use different marketing tactics for later adopters.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,778
Score d'incertitude au seuil0,452

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0030,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,002
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,134
Tête enseignante GPT0,387
Écart entre enseignants0,253 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle