HOW PUBLIC FUNDING AND FIRMS' INNOVATION STRATEGIES AFFECT THE INNOVATION OF THE SPANISH HOTEL INDUSTRY
Bibliographic record
Abstract
ABSTRACTThis article examines the way in which the Spanish hotel industry has responded to government aid and to the use of new technological strategies in order to improve its ability to innovate. Using the Survey on Technological Innovation (Encuesta sobre innovacion tecnologica, 2000), carried out by the Spanish National Institute of Statistics under the guidelines of Eurostat and the Oslo Manual, hotel companies that have carried out innovative activities are investigated. Using the statistical technique of binary regression, we find that public funding has little effect on the innovative performances of these companies, whereas the effect of technological strategies varies depending on whether it is product innovation or process innovation.Keywords: Public financing, innovation strategies, innovation performance, hospitality industry(ProQuest: ... denotes formula omitted.)IntroductionAccording to the Institute for Tourism Studies (IET, 2006), Spain is the second leading tourist destination in the world, preceded only by France and followed by the United States. In terms of tourist income, Spain also comes second, with the U.S. in first place and France in third. We can see from these statistics that Spain is a worldwide power in the tourism industry.The real importance of the tourist sector becomes clear, however, when we consider its internal role in the Spanish economy; in 2007, tourism accounted for 10.8% of Spanish GDP (INE, 2009), a figure much larger than any other single component.Within the tourism sector and its three main components-the hotel industry, travel agencies, and transportation-it is the hotel industry that generates the most income, comprising 65.4% of the total and contributing 7.1% to Spanish GDP (FEHR, 2008).In light of these statistics, an analysis of the Spanish hotel industry is in order to give us an insight into the key factors that determine the competitive abilities of the Spanish tourism industry. The purpose of this article is thus to analyse statistically how public funding and certain technological strategies encourage the hotel industry to innovate in terms of both their products and their processes.The relevance of the present study is clear. On one hand, innovation constitutes one of the keys to competition regardless of the field since a company's competitive capacity to reduce costs depends, to a great extent, on how innovative the process is, how competitive it makes the company compared with others, and on the degree of innovation found in the product. Consequently, the productivity and growth of firms depends crucially on their ability to innovate (Thatcher and Oliver, 2001; Baldwin and Sabourin, 2002; Freeman, 1994; Crafts, 1996).On the other hand, it is important to understand the role of public support for technological development in a sector that is of such great importance to the Spanish economy. In this way, we can learn which strategies for innovation are the most important, thereby pinpointing the strengths and weaknesses of companies in the Spanish hotel industry concerning their plans for innovation.This interest in evaluating the relevance of different innovation strategies on the innovative capacities of companies is a relatively new idea. Until very recently, researchers in favour of the theory of transaction costs (Coase, 1937; Williamson, 1975, 1985) have approached this issue from the assumption that the internal generation of technology (to produce) is at odds with external acquisition (to acquire) (Foray and Mowery, 1990). After the pivotal contribution made by Cohen and Levinthal (1989, 1990) in relation to the theory of absorptive capacity, it has generally been accepted that the coexistence of different innovative strategies within the same organisation can generate a synergistic effect on the results expected in innovation. Therefore, the combination of different strategies is not only possible but also desirable. …
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".