Relationships between Wireless Technology Investment and Organizational Performance
Bibliographic record
Abstract
Information technology (IT) investments are justified based on average improvement in performance (Peacock & Tanniru, 2005). Firms rely on those investments (Demirhan, Jacob, & Raghunathan, 2002; Duh, Chow, & Chen, 2006; Tuten, 2003) because executives believe that investments in wireless technologies help boost company performance. In this regard, the benefits from wireless technology applications depend on the extent to which they are congruent with the firm’s performance (Duh et al., 2006). But, some IS researchers argue that competitors may easily duplicate investments in IT resources by purchasing the same hardware, software, and network, and hence resources necessarily do not provide sustained performance (Santhanam & Hartono, 2003). The use of wireless communications and computing is growing quickly (Kim & Steinfield, 2004; Leung & Cheung, 2004; Yang, Chatterjee, & Chan, 2004). The future of wireless technology may also bring more devices that can operate using the many different standards and it may be possible that a global standard is accepted, such as the expected plans for the 3G technology UMTS. The wireless beyond 3G (B3G) systems or the so called composite radio environments (CRE) (or even 4G systems) possess multiple features that allow employees to collaborate with each other and provide diverse access alternatives (Kouis, Domestichas, Koundourakis, & Theologou, 2007). But issues of risk and uncertainty due to technical, organizational, and environmental factors continue to hinder executive efforts to produce meaningful evaluation of investment in wireless technology (Smith, Kulatilaka, & Venkatramen, 2002). Despite the use of investment appraisal techniques, executives are often forced to rely on instinct when finalizing wireless investment decisions. A key problem with evaluation techniques that emerges is their treatment of uncertainty and their failure to account for the fact that outside of a decision to reject an investment outright, firms may have an option to defer an investment until a later period (Tallon, Kauffman, Lucas, Whinston, & Zhu, 2002). In addition, many authors believe that if firms can combine the appropriate investment strategies to create a unique wireless technology capability, superior firm performance can be the result. Utilization of wireless devices and being “connected” without wires is inevitable (Gebauer, Shaw, & Gribbins, 2004; Jarvenpaa, Lang, Reiner, Yoko, & Virpi, 2003). Market researchers predict that by the end of 2005, there will be almost 500 million users of wireless devices, generating more than $200 billion in revenues (Chang & Kannan, 2002; Xin, 2004). And by 2006, the global mobile commerce (m-commerce) market will be worth $230 billion (Chang & Kannan, 2002). Such predictions indicate the importance that is attached to wireless technologies as a way of supporting business activities. Evaluating investments in wireless technology and understanding which technology makes the “best fit” for a company or organization performance is difficult because of the numerous technologies and the costs, risks, and potential benefits associated with each technology.
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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 | 0.000 | 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 | 0.001 |
| 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".