Bibliographic record
Abstract
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]. …
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 0.002 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".