Notice bibliographique
Résumé
Experimental research has been an important research method in the Information Systems (IS) discipline.Recently, we have seen an expansion in the types of experiments conducted beyond traditional laboratory (lab) and field experiments.These new types of experiments, which leverage the online environment, provide new opportunities as well as new challenges for IS researchers.This diversity also creates the need for authors and reviewers to understand the respective strengths and limitations of various types of experimental research, and not mechanically apply the lens of their favorite type of experiment.The purpose of this editorial is to highlight the reasons that have propelled new types of experiments, categorize these along a set of dimensions, discuss their strengths and weaknesses, and highlight some new issues that emerge with these new opportunities for research.Our objective is not to be exhaustive in terms of the various types of experiments but to highlight opportunities and challenges that emerge for online variants that are more prominently seen in IS research.We, therefore, constrain our focus to lab, field, and natural experiments and their online variants. 1 Changing Landscape of Experiments in IS ResearchExperiments have been a major research method in IS research since the origins of the field.We have recently seen a stronger interest in experiments, especially those occurring online.This can be attributed to the Internet providing two sets of opportunities: (1) a field setting for experimentation as a prominent locus of economic transactions and social interactions (for field and natural experiments), and (b) opportunities to recruit larger subject pools more efficiently and reach more diverse samples with reduced administrative and financial costs (for lab experiments) (Hergueux and Jacquemet 2015).Online transactions and interactions have created both the need and the opportunity for online field experiments to understand the various types of social and economic activities in which people engage online.The availability of persistent trace data for these online transactions and inter-1 Harrison and List (2004) use six criteria to define the "field" context of an experiment: "the nature of the subject pool, the nature of the information that the subjects bring to the task, the nature of the commodity, the nature of the task or trading rules applied, the nature of the stakes, and the nature of the environment that the subject operates in" (p.1012).Based on these characteristics, they classify experiments into four categories: a traditional lab experiment, an artefactual field experiment (lab experiment but with subjects that are representative of the population), a framed field experiment, and a natural field experiment.Their first two categories correspond to lab experiments, whereas the third and fourth categories correspond to field and natural experiments, respectively.
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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,001 |
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 ».