Bibliographic record
Abstract
The role of knowledge as a crucial asset for and enterprise’s survival and advancement has been recognized by several researchers (e.g., von Krogh, Ichijo, & Nonaka, 2000). Moreover, by having knowledge (intellectual resources), an organization can understand how to exploit and develop its traditional resources better than its competitors can, even if some or all of those traditional resources are not unique (Zack, 1999). Therefore, knowledge management (KM-) strategy has to be solidly linked (aligned) to business (B-) strategy in order to create economic value and competitive advantage. Several authors clearly indicate the importance of mutually aligning business strategy and KM efforts and how this alignment helps enhance organizational performance (e.g., Earl, 2001; Ribbens, 1997). For example, Maier and Remus (2001, 2002, 2003) propose a process-oriented approach that considers market-oriented factors in a KM strategy. In this approach KM strategies can be described according to the process focus and type of business processes supported (Maier & Remus, 2001). The process focus can extend from a single business process to an organization-wide perspective, including all relevant business processes (core and service). The type of process is related to the identification of knowledge- intensive business processes. In addition, Sabherwal and Sabherwal (2003) empirically found that the cumulative abnormal stock market return (in the five-day event window) due to a KM announcement is positively associated with the alignment between the firm’s business strategy and the attributes of the KM initiative announced. They use four attributes to characterize KM initiatives: KM level, KM process, KM means, and knowledge source. KM level concerns the hierarchical grouping of individuals upon which the KM effort described in the announcement is focused. The KM processes (or K-manipulating processes) involve the sharing, utilization, or creation of knowledge, while KM means involve organizational structural arrangements and technologies that used to enable KM processes (Earl, 2001; Hansen, Nohria, & Tierney, 1999). Finally, knowledge source reflects from where the knowledge originates. However, realizing the importance of aligning B- and KM-strategies in creating value and in gaining competitive advantage is only the first and the easiest step in any KM initiative. The second and almost as important step is to answer how and where to begin questioning (Earl, 2001). In fact this link has not been widely implemented in practice (see Zack, 1999, and the empirical studies cited there), and “many executives are struggling to articulate the relationship between their organization’s competitive strategy and its intellectual resources and capabilities (knowledge)” (Zack, 1999). This is due to the lack of strategic models to link KMstrategy (knowledge [K-] scope, K-systemic competencies, K-governance, K-processes, K-infrastructures, and K-skills) and business strategy. As Zack (1999) argued, they a need pragmatic yet theoretically sound model. It has been highly accepted that a pragmatic and theoretically sound model should meet at least two criteria. First, it should explicitly include the external domains (opportunities/threat) and internal domains (capabilities/arrangements) of both B- and KM-strategies and the relationships between them. Second, it should provide alternative strategic choices. In order address this issue a “KM strategic alignment model (KMSAM)” is presented. It stems from the premise that the realization of business value gained from KM investment requires alignment between the B- and KM-strategies of the firm and is based on the Henderson-Venkatraman (1993) Strategic Alignment Model for information technology (IT).
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".