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Enregistrement W2158009129 · doi:10.1145/1735223.1735258

IT innovation persistence

2010· article· en· W2158009129 sur OpenAlexaff
Theophanis C. Stratopoulos, Jee‐Hae Lim

Notice bibliographique

RevueCommunications of the ACM · 2010
Typearticle
Langueen
DomaineBusiness, Management and Accounting
ThématiqueERP Systems Implementation and Impact
Établissements canadiensUniversity of Waterloo
Organismes subventionnairesnon disponible
Mots-clésPessimismRevenueValue (mathematics)Panacea (medicine)Investment (military)BusinessOptimismMarketingEconomicsCommerceFinanceComputer science

Résumé

récupéré en direct d'OpenAlex

Introduction While figures vary over time and across industries, the fact is that for most firms, information technology (IT) investments constitute their largest capital-spending item. On average, large US firms spend $300-500 million/year (or 3-4% of total revenue) on IT, with $50-90 million of those dollars invested in new IT products and services. Nevertheless, industrywide managerial attitudes regarding the value of IT innovation have fluctuated over the years. An emerging pattern is characterized by periods of optimism, in which IT innovation is celebrated as a panacea for all business ills, followed by periods of pessimism, in which doubt prevails about the value of investing in new IT, with persisting arguments regarding the ease with which IT can be replicated (Figure 1). In all fairness, in the modern hypercompetitive world it is unlikely that any single investment in IT (or non-IT, for that matter) will lead to a sustained competitive advantage. Instead, what does appear to make a difference is a company's ability to innovate with IT over time. Wal-Mart is a case in point. As Friedman notes, "Wal-Mart … was the first to computerize, the first to use wireless, the first to really deploy RFID … they adopted and adapted faster to new technology than any other retailer in the world. And you've got to give them credit for that. You've got to worry about and be troubled by some of the brutal side of their business practices. But at the end of the day … [they] … out-innovated all their competitors." Similar stories can be found elsewhere in the business world: Harrah's in the entertainment industry, Equifax in credit reporting, RR Donnelley in printing, and Harley-Davidson in the motorcycle industry. What these companies demonstrate---and many innovative IT-adopters corroborate---is that competitors have a hard time imitating and keeping up when a series of IT investments have become integrated with procedural and organizational innovations over the course of several years. In other words, while a single investment in new IT might be easy to copy, it is much more difficult for competitors to replicate a company's ability to innovate with IT over the longer term. This is important for managers to note because those capabilities that are valuable and not easily replicated are more likely to be a source of competitive advantage. To date, only theoretical research has been conducted in this area of IT innovation. In this article, we present empirical results that support the belief that the ability to innovate with IT over time is not easily replicated by competing firms. Given the vast amounts of money currently spent on new IT and, consequently, the high stakes involved in IT innovation initiatives, such evidence is critical. In this article we address the following questions: How likely is it that a firm that has out-innovated its competitors this year will be able to repeat this performance in the following year? In other words, is IT innovation persistent? How do fluctuations in industry-wide managerial attitudes towards IT affect the persistence of IT innovation? Are innovative firms more likely to out-innovate their competitors during periods of managerial optimism or pessimism? How likely is it that a firm will go from a state of non-innovation to being able to out-innovate its competitors within a relatively short period (3-4 years)? In other words, how long does it take for a firm to acquire and develop the ability to out-innovate its competitors?

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,021
Version: metacan-v3-hybrid-931329e0061cStatut 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: aucune
Score de désaccord entre enseignants0,057
Score d'incertitude au seuil0,191

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0030,021
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0030,005
Études des sciences et des technologies0,0020,003
Communication savante0,0100,008
Science ouverte0,0010,005
Intégrité de la recherche0,0020,003
Charge utile insuffisante (le modèle a refusé de juger)0,0570,013

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 Opus0,140
Tête enseignante GPT0,343
Écart entre enseignants0,203 · 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

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations37
Publié2010
Routes d'admission1
Résumé présentoui

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