Bibliographic record
Abstract
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?
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.057 | 0.013 |
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".