A Framework for Evaluating Strategic Location-Based Applications in Businesses
Bibliographic record
Abstract
Abstract Location-based and location-oriented applications will be amongst the most powerful drivers of organizational change in the coming years. However, the strategic use of these technologies and their benefits will vary from one organization to another based on the business' needs. The alignment between the use of location-based technologies and the firms' business strategic orientation is thus of primary importance. In an attempt to support firms in this alignment process, this paper presents the GEOGRID framework for identifying and analyzing the strategic opportunities offered to firms by the use of leading edge location technologies. This framework groups the main variables that need to be considered in order to analyze the competitive positioning of the business, to reveal assumptions, to ascertain benefits, and to control costs tied to the strategic applications of location technologies in organizations. Resumes Les applications utilisant la localisation et comportant des composantes localisation seront parmi les plus importants leviers de changements organisationnels au cours des prochaines annees, Cependant, l'utilisation strategique de ces technologies et leurs benefices vont varier d'une entreprise a une autre selon les besoins organisationnels de chacune. L'alignement entre ces technologies de localisation et les orientations strategiques des entreprises est d'importance primordiale. Pour tenter de mieux appuyer ce processus d'alignement, cet article presente le cadre GEOGRID pour identifier et analyser les possibilites strategiques offertes aux entreprises par l'introduction de technologies de localisation de pointe. Ce cadre regroupe les variables principales qui doivent etre prises en compte pour analyser la position concurrentielle de l'entreprise, pour degager les hypotheses, pour identifier les benefices et pour controler les couts des applications strategiques des technologies de localisation dans les entreprises. ********** To this day, very few information technologies have both a strategic impact on organizations and an important location dimension. After briefly reviewing these technologies, this paper focuses on the dimensions that must be taken into account by firms before implementing these information technologies and proposes a grid for analyzing their strategic impacts and their contribution to the strategic direction of organizations. Current Trends in Location-Based IT and Their Impacts on Organizations Three information technologies, ubiquitous or pervasive computing, mobile and wireless networks, and location-based technologies are presently making very rapid strides and bringing important and continuous changes in today's organizations (Sieworek 2002; Borriello et al 2005). The location-based applications and technologies discussed in this paper are at the intersection of the three sets of technologies presented in Figure 1. [FIGURE 1 OMITTED] Ubiquitous computing: Ubiquitous computing, sometimes described as pervasive computing, is the new state of technologies that are ever present and always available in today's business environment. Be it by wire or wireless, portable communications, desktop, portable computers or PDA, computer processing power and information is always available to the decision maker. Ubiquitous computing is redefining the client-business relationship: businesses can now know their customers' and suppliers' location and their preferences. In this way, they are becoming more capable of satisfying their business partners needs, which will allow them to maintain deeper and more profitable business relations. Mobile communications and wireless networks: In recent years, there have been tremendous strides in mobile and wireless communications technologies Portable telephones, RFID (Radio Frequency Identification) and wireless local networks have penetrated the business environment and have changed the way people in organizations communicate, get their information and manage people. …
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".