Notice bibliographique
Résumé
1. IntroductionThe number of online sales is increasing dramatically, and every minute, countless online-advertising messages compete for online shoppers' attention. For this reason, online retailers face tough challenges in attracting target customers, especially when they have opted to use online sales promotions to increase visibility and to enhance competitiveness. In the field of communication tactics, several studies have demonstrated the increasing popularity exhibited by online sales promotions that influence consumers' decision making [e.g., Buil et al. 2013; Christou 2011; Crespo-Almendros & Del Barrio-Garcia 2014; Prendergast et al. 2013; Xu & Huang 2014].In general, online sales promotions include monetary tools, such as e-coupons and discounts, and non-monetary tools, such as contests, sweepstakes, and lucky draws [Chandon et al. 2000]. This study focuses on non-monetary-based tools, especially in the case of online lucky draw promotional tactics. This focus reflects the fact that online lucky draws have to comply with effort requirements and rely on chance; indeed, mere participation in such games is in itself sometimes considered by online shoppers to be enjoyable beyond any price savings that may be offered [Chandon et al. 2000]. Moreover, marketers have realized that lucky draws provide high value but use a limited amount of the promotional budget to reward customers [Palazon & Delgado-Ballester 2009], and such games can also easily be administered online and can be effective at driving traffic to online retailers' websites [Smith 2009], ultimately increasing brand equity [Buil et al. 2013]. To date, however, only limited academic e-commerce research has been devoted to the strategy of offering online lucky draws.An effective promotional program has to consider two factors: campaign characteristics and individual traits. Some promotional programs, such as online lucky draws, share a common underlying structure whereby customers need to comply with effort requirements to have the chance of earning rewards [Kivetz 2003] . Effort requirements and the earning of possible rewards are the two main characteristics in online lucky draw campaigns. For example, in one case online shoppers are told that they may receive a specific reward for participating in an online lucky draw campaign (e.g., Complete this survey today and you may win a notebook). In another case, online shoppers a re told that they may receive a mystery reward for participating in an online lucky draw campaign (e.g., Play this game today and you may win a mystery gift).For online shoppers, the chance of winning a mystery gift would be comparably more uncertain than the chance of winning a notebook possessing an explicit value. Prior studies have divided products or, more specifically, known rewards into two types: hedonic and utilitarian [e.g., Botti & McGill 2011; Palazon & Delgado-Ballester 2013]. The present study places online lucky draw campaigns' rewards into at least one of three categories: hedonic, utilitarian, and mystery. It is quite surprising that no research attention has been given to the three kinds of rewards offered as an incentive in online lucky draw campaigns while, in today's e-commerce, online shoppers are being offered a wide variety of incentives. Thus, a fundamental question in this research is which types of rewards will enhance evaluations of online lucky draws. An understanding of how the various rewards enhance or undermine evaluations of online lucky draws is critical for differentiating between online sales promotions.Prior studies have classified effort requirements as either small or large and as either interesting or boring [Kivetz 2003; Kivetz & Simonson 2002; Soman 1998]. Such effort requirements have a predictable effect on reward preferences [Kivetz 2003], while customers also use effort requirements to justify choosing a luxury reward over a utilitarian reward [Kivetz & Simonson 2002]. …
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.
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 | 0,013 | 0,014 |
| 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 | 0,000 |
| Science ouverte | 0,002 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».