Hasil untuk "Business"

N. Ismail

S. Schaltegger, Florian Lüdeke‐Freund, E. Hansen

G. George, A. Bock

D. Karlan, Martín Valdivia

P. Eriksson, Anne Kovalainen

PART ONE: THE BUSINESS OF QUALITATIVE RESEARCH Introduction Research Philosophy Research Design and Process Focus and Frame Access and Relationships Ethics in Research Qualitative Research Materials Electronic Research PART TWO: METHODS IN QUALITATIVE BUSINESS RESEARCH Case Study Research Ethnographic Research Grounded Theory Research Focus Group Research Action Research Narrative Research Discourse Analysis Feminist Research Critical Research PART THREE: WRITING AND EVALUATING QUALITATIVE BUSINESS RESEARCH Writing Process Qualitative Research Evaluation Closing Up

Wendy K. Smith, M. Gonin, Marya L. Besharov

M. Hughes, R. Morgan

Abstract Studies of entrepreneurial orientation tend to examine its three most common features only (risk-taking, innovativeness, and proactiveness), merging these into a gestalt construct of entrepreneurial orientation and then analyzing its effect on business performance. This is in contrast to Lumpkin and Dess who stressed an entrepreneurial orientation is best characterized by five dimensions which can vary independently and may not be equally valuable across performance metrics or at different stages of development. We rectify these problems by examining the independent impact of risk-taking, innovativeness, proactiveness, competitive aggressiveness, and autonomy on performance of young high-technology firms at an embryonic stage of development. Our results support the concerns of Lumpkin and Dess. Only proactiveness and innovativeness have a positive influence on business performance while risk-taking has a negative relationship. Competitive aggressiveness and autonomy appear to hold no business performance value at this stage of firm growth. From these results, we offer implications for managers in addition to guidance for future research.

M. Merz

J. Brüderl, Peter Preisendörfer

Yolande E. Chan, S. Huff, D. W. Barclay et al.

P. Patterson, Richard A. Spreng

Anil K. Gupta, V. Govindarajan

I. Maignan, O. Ferrell, G. Hult

E. Mendoza

R. Lucas

C. DaSilva, P. Trkman

A. Massis, J. Kotlar

P. Burns

Alexis Pengfei Zhao, Mohannad Alhazmi, Shuangqi Li et al.

Abstract Wildfires pose a significant threat to urban regions, with cities like Los Angeles facing increasing challenges due to their vulnerability to frequent and severe wildfire events. This study proposes a novel framework for optimizing fire rescue vehicle scheduling and energy system operations during wildfire disasters. By integrating predictive wildfire modeling with microgrid-based energy systems, the framework dynamically allocates energy resources to critical demands such as emergency shelters, hospitals, and rescue operations when grid supply is disrupted. The wildfire model simulates fire growth, wind-driven spread, and infrastructure impact, ensuring that the framework adapts to real-time conditions. A case study focusing on Los Angeles demonstrates the practical application of the proposed methodology, showcasing improved emergency response, minimized infrastructure losses, and enhanced operational efficiency during wildfires. This research highlights the importance of combining energy systems and disaster management strategies to build resilience in wildfire-prone urban areas, offering valuable insights for emergency planners and policymakers.

Yujia Bao, Ankit Parag Shah, Neeru Narang et al.

This paper introduces Fortune Analytics Language Model (FALM). FALM empowers users with direct access to comprehensive business analysis, including market trends, company performance metrics, and expert insights. Unlike generic LLMs, FALM leverages a curated knowledge base built from professional journalism, enabling it to deliver precise and in-depth answers to intricate business questions. Users can further leverage natural language queries to directly visualize financial data, generating insightful charts and graphs to understand trends across diverse business sectors clearly. FALM fosters user trust and ensures output accuracy through three novel methods: 1) Time-aware reasoning guarantees accurate event registration and prioritizes recent updates. 2) Thematic trend analysis explicitly examines topic evolution over time, providing insights into emerging business landscapes. 3) Content referencing and task decomposition enhance answer fidelity and data visualization accuracy. We conduct both automated and human evaluations, demonstrating FALM's significant performance improvements over baseline methods while prioritizing responsible AI practices. These benchmarks establish FALM as a cutting-edge LLM in the business and media domains, with exceptional accuracy and trustworthiness.