Hasil untuk "Business"

M. Ayyagari, T. Beck, A. Demirguc-Kunt

H. Brandstätter

S. Winter, Gabriel Szulanski

C. Fine

M. Beer, N. Nohria

G. Stalk, P. Evans, L. Shulman

A. Neely

J. Coyle, E. Bardi

H. Stevenson, Michael J. Roberts, H. I. Grousbeck

Yunkai Lou, Shunyang Li, Longbin Lai et al.

Business Intelligence (BI) analysis is evolving towards Exploratory BI, an iterative, multi-round exploration paradigm where analysts progressively refine their understanding. However, traditional BI systems impose critical limits for Exploratory BI: heavy reliance on expert knowledge, high computational costs, static schemas, and lack of reusability. We present ExBI, a novel system that introduces the hypergraph data model with operators, including Source, Join, and View, to enable dynamic schema evolution and materialized view reuse. Using sampling-based algorithms with provable estimation guarantees, ExBI addresses the computational bottlenecks, while maintaining analytical accuracy. Experiments on LDBC datasets demonstrate that ExBI achieves significant speedups over existing systems: on average 16.21x (up to 146.25x) compared to Neo4j and 46.67x (up to 230.53x) compared to MySQL, while maintaining high accuracy with an average error rate of only 0.27% for COUNT, enabling efficient and accurate large-scale exploratory BI workflows.

C. R. Anderson, C. Zeithaml

Nohayla Badrane, Zineb Bamousse

In a rapidly evolving landscape marked by continuous change and complex challenges, effective cash management stands as a cornerstone for ensuring business sustainability and driving performance. To address these pressing demands, cash managersare increasingly turning to innovative financing solutions such as venture capital, green finance, crowdfunding, advanced services from Pan-African banks, and blockchain technology. These cutting-edge tools are pivotal in bolstering resilience against market volatility, ecological transitions, and the accelerating pace of technological change. The present article aims to examine how such innovative financial approaches can serve as strategic drivers, enabling businesses to transform challenges into opportunities. The analysis underscores that rethinking cash management through innovation is a critical pathway toboost the performance of Moroccan companies. Therefore, embracing these forward-thinking strategies unlocks new avenues for development empowering them to adapt with agility amidst the uncertainties of a shifting environment.

Dariusz Baran, Ernest Górka, Michał Ćwiąkała et al.

This paper examines the impact of internal communication on effective business management through a comparative analysis of two medium-sized car rental companies operating in Poland. Using a structured survey completed by 220 employees, the study evaluates 15 communication-related factors, including feedback culture, managerial accessibility, message clarity, and interdepartmental coordination. The findings indicate that Company X significantly outperforms Company Y across all evaluated dimensions, largely due to its use of advanced communication technologies, participatory models, and clear feedback mechanisms. The research highlights the strategic role of two-way communication in fostering employee engagement, organizational transparency, and operational efficiency. It contributes to the field by offering a rare, data-driven comparison within one industry and supports existing models that link internal communication to job satisfaction and motivation. Limitations include reliance on self-reported data and focus on a single industry and country. Future studies are recommended to explore cross-sector and longitudinal perspectives, especially in the context of digital and hybrid work environments.

Yusen Wu, Xiaotie Deng

This paper proposes DeepRule, an integrated framework for automated business rule generation in retail assortment and pricing optimization. Addressing the systematic misalignment between existing theoretical models and real-world economic complexities, we identify three critical gaps: (1) data modality mismatch where unstructured textual sources (e.g. negotiation records, approval documents) impede accurate customer profiling; (2) dynamic feature entanglement challenges in modeling nonlinear price elasticity and time-varying attributes; (3) operational infeasibility caused by multi-tier business constraints. Our framework introduces a tri-level architecture for above challenges. We design a hybrid knowledge fusion engine employing large language models (LLMs) for deep semantic parsing of unstructured text, transforming distributor agreements and sales assessments into structured features while integrating managerial expertise. Then a game-theoretic constrained optimization mechanism is employed to dynamically reconcile supply chain interests through bilateral utility functions, encoding manufacturer-distributor profit redistribution as endogenous objectives under hierarchical constraints. Finally an interpretable decision distillation interface leveraging LLM-guided symbolic regression to find and optimize pricing strategies and auditable business rules embeds economic priors (e.g. non-negative elasticity) as hard constraints during mathematical expression search. We validate the framework in real retail environments achieving higher profits versus systematic B2C baselines while ensuring operational feasibility. This establishes a close-loop pipeline unifying unstructured knowledge injection, multi-agent optimization, and interpretable strategy synthesis for real economic intelligence.

Matthias Stierle, Karsten Kraume, Martin Matzner

Data-driven analysis of business processes has a long tradition in research. However, recently the term of process mining is mostly used when referring to data-driven process analysis. As a consequence, awareness for the many facets of process analysis is decreasing. In particular, while an increasing focus is put onto technical aspects of the analysis, human and organisational concerns remain under the radar. Following the socio-technical perspective of information systems research, we propose a new perspective onto data-driven process analysis that combines the process of analysis with the organisation and its stakeholders. This paper conceptualises the term process analytics and its various dimensions by following both an inductive and deductive approach. The results are discussed by contrasting them to a real-life case study from a large company implementing data-driven process analysis and automation.

Martin Käppel, Julian Neuberger, Felix Möhrlein et al.

Predictive process monitoring enables organizations to proactively react and intervene in running instances of a business process. Given an incomplete process instance, predictions about the outcome, next activity, or remaining time are created. This is done by powerful machine learning models, which have shown impressive predictive performance. However, the data-driven nature of these models makes them susceptible to finding unfair, biased, or unethical patterns in the data. Such patterns lead to biased predictions based on so-called sensitive attributes, such as the gender or age of process participants. Previous work has identified this problem and offered solutions that mitigate biases by removing sensitive attributes entirely from the process instance. However, sensitive attributes can be used both fairly and unfairly in the same process instance. For example, during a medical process, treatment decisions could be based on gender, while the decision to accept a patient should not be based on gender. This paper proposes a novel, model-agnostic approach for identifying and rectifying biased decisions in predictive business process monitoring models, even when the same sensitive attribute is used both fairly and unfairly. The proposed approach uses a human-in-the-loop approach to differentiate between fair and unfair decisions through simple alterations on a decision tree model distilled from the original prediction model. Our results show that the proposed approach achieves a promising tradeoff between fairness and accuracy in the presence of biased data. All source code and data are publicly available at https://doi.org/10.5281/zenodo.15387576.

Fang Wang, Paolo Ceravolo, Ernesto Damiani

Predictive Business Process Monitoring (PBPM) aims to forecast future outcomes of ongoing business processes. However, existing methods often lack flexibility to handle real-world challenges such as simultaneous events, class imbalance, and multi-level attributes. While prior work has explored static encoding schemes and fixed LSTM architectures, they struggle to support adaptive representations and generalize across heterogeneous datasets. To address these limitations, we propose a suite of dynamic LSTM HyperModels that integrate two-level hierarchical encoding for event and sequence attributes, character-based decomposition of event labels, and novel pseudo-embedding techniques for durations and attribute correlations. We further introduce specialized LSTM variants for simultaneous event modeling, leveraging multidimensional embeddings and time-difference flag augmentation. Experimental validation on four public and real-world datasets demonstrates up to 100% accuracy on balanced datasets and F1 scores exceeding 86\% on imbalanced ones. Our approach advances PBPM by offering modular and interpretable models better suited for deployment in complex settings. Beyond PBPM, it contributes to the broader AI community by improving temporal outcome prediction, supporting data heterogeneity, and promoting explainable process intelligence frameworks.

Ana-Maria Coatu, Felix-Angel Popescu, Laurențiu Petrila

This study explores how socio-economic factors affect the effectiveness of public accountability frameworks in EU member states, with Romania as a case study. Using data from the World Bank, Eurobarometer, and cross-country comparisons, it identifies five key determinants: income inequality, education, healthcare access, political participation, and economic stability. Grounded in institutional theory, the research shows that inclusive institutions and lower disparities lead to stronger accountability, while weaker frameworks often reinforce inequality and corruption. For Romania, the study recommends boosting transparency, enforcing anti-corruption measures, improving rural-urban equity, and enhancing civic education to strengthen the link between citizens and institutions.

Gholamreza Soleimani Amiri, Neda Pourgholamreza

Objective: “The purpose of this study is to investigate the effect of auditor industry specialization on audit fees and audit report lag. In addition, this study examines the effect of audit report lag on the relationship between auditor industry specialization and audit fees”. Method: “In this research, the data of 132 companies admitted to the Tehran Stock Exchange during the period from 2014 to 2023 were used. Also, in this research, Standard Audit Fee Model and multivariate linear regression with fixed effects has been used”.Results: “The results showed that the auditor industry specialization does not affect the audit fee. However, the auditor industry specialization has a significant effect on the audit fees by mediating the audit report lag. Also, the results have shown a significant negative effect of the auditor's specialization in the industry on the audit report lag”.Conclusions: “In general, this research shows that companies that contract with audit firms with specialization in the industry pay less due to the expertise of the audit firm and the timeliness and brevity of their audit reports”.

John Welsh, Jerry White, Phil Dowell