Analytics, Volume 5, Issue 2 (June 2026) – 3 articles

Narrative extraction builds coherent ordered sequences of documents that trace how concepts develop over time, and is a growing area of information retrieval. In this work we focus on scientific literature, using a corpus of 3549 IEEE visualization research papers (1990–2022). A natural hypothesis is that augmenting embedding-based pathfinding with explicit domain knowledge should improve narrative quality. We present the Knowledge-Coherence Framework (KCF), which integrates structured metadata from OpenAlex into narrative extraction (building on the Narrative Trails algorithm), and conduct a systematic empirical investigation along three axes: (1) the effect of embedding model choice (MiniLM vs. SPECTER), (2) the effect of knowledge augmentation (with and without, plus sensitivity to the knowledge weight $\alpha$), and (3) the reliability of LLM-based evaluation (cross-agreement among 13 large language models). Throughout, mathematical coherence denotes the geometric mean of angular and topic similarity between consecutive documents along a path—an automatic, model-computed quantity inherited from Narrative Maps and Narrative Trails—while narrative quality refers to the LLM-judged construct. Using up to 600 evaluation pairs, we find that embedding model choice has a large effect on mathematical coherence (SPECTER: 0.94 vs. MiniLM: 0.81) and that, contrary to expectations, knowledge augmentation does not improve LLM-judged narrative quality—it slightly decreases it for both embeddings. Notably, the two notions dissociate: SPECTER produces the most mathematically coherent paths, yet MiniLM paths receive the highest LLM narrative-quality scores (5.87 vs. 5.36 out of 10). Alpha sensitivity analysis over five values ($\alpha \in \{0.0,0.3,0.5,0.7,1.0\}$, 500 pairs) confirms that LLM scores remain essentially flat while mathematical coherence steadily declines with increasing knowledge weight. Cross-model evaluation with 13 LLM judges shows high inter-model agreement (median Pearson $r=0.71$), supporting evaluation reliability. The main practical takeaways are that (i) embedding model choice, not knowledge augmentation, is the more consequential design decision, and (ii) mathematical coherence and LLM-judged narrative quality are distinct optimization targets that practitioners should not conflate. Full article

This article examines the requirements for the successful deployment of business analytics in industry and uses this as a framework to provide a business intelligence perspective on the demise of a case study company, drinks manufacturer HP Bulmer Ltd., resulting in the collapse and takeover of the company in 2003. Based on a scoping literature review and a qualitative interpretivist approach, the article investigates the critical success factors for business analytics software projects and classifies these into five main organisational pillars that are required for successful analytics deployment. Then, using documents available in the public domain, the article examines the case study of HP Bulmer Ltd., which used analytics software in the 1990s and early 2000s as the company attempted to establish itself as a global drinks manufacturer. The article reports on how the company struggled to put the necessary pillars in place for successful use of their analytics systems, but having finally achieved this, then failed to take the necessary decisions to steer the company towards profitability as opposed to rapid growth in turnover. The article uses the case study to reflect on the key aspects of analytics technology deployment and the wider field of digitalisation and digital transformation, and points to the critical importance of political will to formulate and steer data-informed strategy. The research contributes to the development of theory regarding analytics deployment and will be of value to practitioners faced with the challenges of implementing analytics systems in industry. Full article

This study explores the intricate behavioral consumer psychology dynamics of how certain elements—color, price, gender differences, and the concept of the frequency illusion—affect emotions, brand awareness, and consumer decision-making in a digital environment. Going beyond conventional analyses, this study also explores the intersection of sustainable business practices, elucidating the potential for ethical, environmentally conscious, and business-sustainable decision-making. Utilizing a quantitative method and survey data from 207 respondents, this research contributes to a more profound level of understanding of consumer decision-making in the Lebanese retail sector, offering strategic insights for organizations seeking to enhance brand recognition, while aligning with responsible and sustainable practices in today’s dynamic and competitive environment. The study found that psychological cues—color, price, gender differences, and frequency illusion—significantly influence emotions, brand awareness, and consumer decision-making in retail. Future research should examine the tensions in consumer decision-making, where brand awareness and emotional cues can simultaneously facilitate and bias choices, with effects contingent on exposure, demographic characteristics, digital fluency, and cultural context. Full article