The Importance of Human Domain Knowledge and Business Data Analytics to Support Modern Financial Decisions
Bibliographic record
Abstract
The purpose of this study is to identify how importance is human domain knowledge and business data analytics to support modern financial decision. Understanding whether social media narratives could provide a value-add to current customer relationship management practices could be quite valuable. Design/methodology/approach-An analysis of the literature was undertaken and based on an assessment of the literature, conceptual states and pragmatic approaches as well as existing theoretical understandings and frameworks. An explorative case study approach based on Yin’s design will be utilized as a framework as well as a demographic survey to distill even further the characteristics of the sampling from a customer, management and social media user perspective. Furthermore, a customer relationship management framework which would include the adjoining of data analytics and social media narratives will be discussed in context of the research findings. This will help researchers and practitioners more readily explore the shared value framework which the study will be based and contribute to a more fulsome consideration of customer relationship management practice shifts within a technological and social media-oriented age. The contributions of this research will also help reiterate the importance of context in data management as well as the importance of the paradigmatic power shifts reflected in consumer usage of social media, product or service offerings, social consciousness and ethical practice as it relates to the influence of consumer intentions and subsequent purchasing intentions.The purpose of this qualitative exploratory case study was to gain common understandings of how importance is human domain knowledge and business data analytics to support modern financial decision. In order to support reliable and valid research, a purposive sample of customer relations managers, business analysts who have customer relations management (CRM) roles, and customers who utilize social media for the purposes of product or service development was attained.
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 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 | 0.015 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".