Selecting New IT Capability: RFID Systems for Process Industry
Bibliographic record
Abstract
Information technology (IT) innovation imparts strategic and competitive benefits to an organization. In spite of the focus on technological, organizational and environmental factors, many researchers have acknowledged that whether, when and how to innovate with IT is a complex and crucial question faced by managers in most of the organizations. While decision-makers are faced with a complex decision-making scenario of deciding to adopt a technology that is relatively new and uncertain in terms of expected outcomes, it also calls for large resource investments, or embrace the risk of becoming weighed down with outdated technology, and losing the flexibility to deploy new IT capability based on market requirements. This research attempts to understand the decision-making process that managers go through in the adoption of radio frequency identification (RFID) technology.RFID is a technology that use radio frequency to communicate data and identify objects automatically. It is an emerging technology intended to replace traditional barcodes in many ways. Due to the increasing demand for automatic identification, organizations turned to RFID to help them achieve their goals. Replacing barcodes with RFID has the primary effects of reducing labour costs, improving total product traceability and increasing accuracy. RFID technology can be widely used in variety of areas such as, animal tagging, waste management, access control, passports, drug industry, and manufacturing automation. There is an accelerating trend in the uses of RFID systems for several applications. Implementations of RFID systems are critical investment projects because of technological problems, adaptation risks and high cost. One of the major issues concerning RFID implementation is selecting the optimal RFID system and the provider that best fits the firm's requirements. Hence, an analytical model for selecting the RFID systems among alternatives can be very essential to study. Therefore, this paper aims is to develop a multi criteria decision support system for selecting the most appropriate RFID system. Decision makers evaluated three RFID integrators among different criteria and chose the most suitable RFID system for an existing company. A process industry case study has been considered to illustrate the methodology.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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".