Market and Bureaucracy Costs: The Moderating Effect of Information Technology
Notice bibliographique
Résumé
INTRODUCTION Gathering information has been a function of managers since the days of Barnard (1938) because, as suggested by Porter (1979), the information about the environment that is available to an organization affects the efficiency of the strategy it chooses to employ. For this reason, one structural variable that is receiving a great deal of attention is information technology(IT) (Davis, 1991; Fiedler, Grover, & Teng, 1996; Radhakrishnan, Zu, & Grover, 2008; Soh, Markus, & Goh, 2006), especially as IT has become affordable for even the smallest of firms (Unknown, 2003). One problem in the strategy-IT literature, however, is that, in most instances, only specific components of information technology are examined. For example, a review of the last few years' strategy-IT literature has shown investigations into reverse auctions(Mithas, Jones, & Mitchell, 2008), supply chain management (Jean & Sinkovics, 2008), outsourcing (Rustagi, King, & Kirsch, 2008), use of the World Wide Web (Bemslimane, Plaisent, & Bernard, 2005), knowledge management (McGill, 2007) and interorganizational systems (Han, Kauffman, & Nault, 2008). Very few (e.g., Radhakrishnan, Zu, & Grover, 2008) deal with complete IT systems and their uses. Strategy researchers disagree which is the most appropriate theoretical paradigm to use to explain business performance (e.g. Kristensen & Lojacono, 2002; Williamson, 2008; Doty, Glick, & Huber, 1993; Tiwana & Bush, 2007), thus another problem that arises in the literature is that many different theories are proposed to explain the impact of IT systems on strategy and viceversa. These include such varied themes as Miles and Snow's (1978) organizational taxonomy (Karimi, Gupta, & Somers, The Congruence between a Firm's Competitive Strategy and Information Technology Leader's Rank and Role, 1996), Hambrick and Mason's (1984) upper echelon theory (Leonard & Dooley, 2007), social embeddedness (Chatfield & Yetton, 2000), transaction cost economics (Brynjolfsson, Malone, Gurbaxani, & Kambil, 1994; Jean & Sinkovics, 2008), trust (Rustagi, King, & Kirsch, 2008), resource based view (Radhakrishnan, Zu, & Grover, 2008), and punctuated equilibrium (Lassila & Brancheau, 1999) to name but a few. However, there does appear to be a common theme in much of the IT literature: efficiency. Efficiency is either directly discussed or implied in much of the IT material, regardless of the theoretical approach taken. Considering that transaction cost economics has efficiency as its underlying foundation (Williamson, Markets and Hierarchies: Analysis and Antitrust Implications, 1975), it would make sense that TCE could be used to explain and predict the relationship between information technology and strategic choice. THEORY & PROPOSITIONS The strategy a firm adopts, according to transaction cost economics, depends upon the costs associated with that strategy. In cases where the chosen strategy of the firm does not provide the optimum available reduction of transaction costs (i.e., efficiency), performance suffers. Thus, when the costs of transacting in the market are high or the market fails, transactions will be brought in-house and a hierarchical governance mechanism will be used. Conversely, when the costs of transacting in the market are low, a market system will be chosen. However, the strategy of the firm leads to high performance only when the structure that the firm adopts optimizes the transaction costs associated with the chosen strategy (Williamson, Markets and Hierarchies: Analysis and Antitrust Implications, 1975). TCE, therefore, follows the accepted model of strategy --structure--performance (e.g., Williamson, 1975; Porter, 1980; Miles & Snow, 1978; Abernethy & Lillis, 2001; Jones & Hill, 1988). Gurbaxani and Whang (1991) have suggested that all transaction costs result in one way or another from lack of information. …
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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 | 0,009 | 0,001 |
| 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 | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,003 |
| 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 | 0,000 |
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 ».