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The Impact of Predictive Analytics on Business Decision-Making in UK…
The Impact of Predictive Analytics on Business Decision-Making in UK Organisations
Research Focus: Predictive Analytics (PA) Impact on Decision-Making in UK
Gap Statement:
Limited empirical evidence on PA's value across diverse UK sectors.
Most studies focus on large firms, global context, or theoretical hurdles.
Annotated Literature Review Themes
Theme: Analytics Capability
Source: Cao, Duan & Li (2015)
Summary: PA improves decision-making by enhancing information processing (forecasting, strategic insight).
Gap: Focused only on large firms; limited sector diversity.
Theme: Culture & Leadership
Source: Dubey et al. (2019)
Summary: PA works better when backed by strong data-driven culture and leadership commitment.
Gap: No specific UK organisational context examined (global sectors).
Theme: Human Judgement / Integration
Source: Orjatsalo (2022)
Summary: PA is best used when combined with human intuition/judgement; organisations must integrate technology and intelligence.
Gap: Lack of UK-specific insight.
Theme: Barriers to Adoption
Source: Finlay (2014)
Summary: Skill shortages, poor data governance, and misunderstanding hinder PA adoption.
Gap: Mostly theoretical; little UK empirical evidence. Barriers still burden UK firms (especially smaller ones).
Theme: Industry Evidence
Source: PwC (2023)
Summary: Shows increased decision speed and efficiency through analytics in UK businesses (63% report improvement).
Gap: Not academically rigorous; correlation but not causation (often self-reported data).
Conceptual Framework Variables
Independent Variable (Adoption & Capability)
Focus: Effectiveness of exploiting PA tools, quality of analytics system, employee skill level.
Involves: Forecasting, modelling, trend analysis, statistical algorithms.
Dependent Variable 1 (Decision-Making Process)
Focus: How managers look at information, alternatives, and decisions.
Improved by PA: Eliminating uncertainty, accelerating speed, increasing accuracy/confidence.
Dependent Variable 2 (Business Performance Outcomes)
Focus: Reflections of better decision-making.
Examples: Better forecasting, reduced costs, enhanced operating capabilities, improved customer service.
Moderating Factors (Strengthen/Weaken the relationship)
Factor 1: Organisational Culture
Influence: Willingness to accept and trust analytics (supportive culture strengthens PA).
Factor 2: Data Quality
Influence: Reliability of data (good data increases effects; bad data diminishes results).
Factor 3: Leadership Support
Influence: Allocation of resources, training, and strategic direction (fosters PA adoption and use).