Market and Bureaucracy Costs: The Moderating Effect of Information Technology
Bibliographic record
Abstract
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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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 | 0.005 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.046 | 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".