Bibliographic record
Abstract
The authors perform an 'industry analysis' for higher education, using the five forces model of M.E. Porter. Although strategic planning has fallen out of favor in many business organizations (Jelinek 1979; Welch and Welch 2005), it remains the primary means for strategy making on virtually every college campus in the United States. The higher education literature on strategic planning ranges from the conceptual (Peterson, Dill, and Mets 1997) to the practical, including step-by-step instructions for completing the process (Rowley, Lujan, and Dolence 1997). Strategic planning is an important tool, but higher education's sole dependence on it has come at the expense of other useful instruments in the strategy making process. For instance. Porter's (1980) five forces model, which sets the standard for industry analysis, can complement strategic planning and thus contribute to a more comprehensive organizational strategy. An industry analysis using Porter's model pays particular attention to five forces that influence any industry: threat of new entrants, intensity of rivalry, threat of substitutes, bargaining power of buyers, and bargaining power of suppliers. Current examples of the model's application vary from exploring leadership and differentiation in professional service firms (Ou and Chai 2007) to understanding the Internet's role in changing markets and entire industries (Karagiannopoulos, Georgopoulos, and Nikolopoulos 2005). In light of the ongoing interest in the five forces model in diverse fields outside of higher education, we ask two questions. First, does industry analysis provide insight into higher education? Second, does Porter's emphasis on the five forces sufficiently describe the environment in which higher education institutions function? In addition to answering these questions, we highlight where industry analysis complements and/or compares to strategic planning throughout the article. Applications of the Five Forces Model Academicians from a variety of disciplines have used Porter's five forces model to describe different industries. Ondersteijn, Giesen, and Huirne (2006) conducted an industry analysis using Porter's model to interpret the external context of Dutch dairy farming. Fratto, Jones, and Cassili (2006) employed it to better understand apparel retailers and price competition within the apparel industry. Siaw and Yu (2004) were interested in the impact of the Internet on banking competition. Pines's (2006) application of the model to emergency medicine allowed him to develop a set of recommendations for how players within the field - including emergency departments and physicians might better work together to strengthen their services and ultimately offer improved care. In an article about building a firm's lobbying strategy, Vining, Shapiro, and Borges (2005) used Porter's model to identify the environmental context in which the firm operates. Dobni and Dobni (1996) provide one of the few examples of an industry analysis in higher education planning. They applied Porter's model to Canadian university-based business schools and uncovered pathologies that were incompatible with the realities for which they were supposedly preparing their students. The relative absence of industry analysis in college and university planning suggests a need for more serious consideration and application in the field. A New Era for the Higher and Postsecondary Education Industry Peterson and Dill (1997) defined three eras that characterize the evolution of the higher education industry: traditional higher education, mass higher education, and postsecondary education. We contend that higher education has moved into a fourth era that brings with it immense pressure from organizations that in the past offered little competition for students and resources. International institutions now compete with U.S. colleges and universities for students. Corporations and private companies deliver training, education, or a mixture of the two. …
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.028 | 0.008 |
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".