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Record W340323259

The Art of M&A Strategy: A Guide to Building Your Company's Future Through Mergers, Acquisitions, and Divestitures

2012· book· en· W340323259 on OpenAlexaboutno aff
Kenneth Smith, Alexandra Reed Lajoux

Bibliographic record

VenueMedical Entomology and Zoology · 2012
Typebook
Languageen
FieldBusiness, Management and Accounting
TopicInternational Business and FDI
Canadian institutionsnot available
Fundersnot available
KeywordsRestructuringMergers and acquisitionsDivestmentCompetitive advantageGlobalizationContext (archaeology)Strategic planningBusinessConsolidation (business)Strategic managementGlobal strategyMarketingEconomicsAccountingMarket economyFinance
DOInot available

Abstract

fetched live from OpenAlex

Seize the competitive advantage with today's most powerful strategic tool: mergers and acquisitions Driven by rapid globalization, technological advances, and dramatic changes in public policy, industry restructuring has become as usual. Yet the failure rates in MA participating in industry consolidation; corporate growth; and using acquisitions create real Some well-known historic examples are used illustrate success in strategies of each type that have been achieved through M&A. Part II is a to guide the development and execution of a successful M&A strategy. It begins with a review of strategic planning and outlines how determine the role of M&A, considering the industry context, the competitive imperatives and strategy options. If M&A is selected as one of the strategic weapons, subsequent chapters then outline how search and screen partner candidates, how choose between buying and selling, and finally how the board of directors should be engaged in M&A decisions. Part III goes from M&A as an event sustained strategic M&A programs. In particular, it examines the implications of globalization and the restructuring of industries on a global basis. When M&A is central strategy, competitive M&A skills are essential, so the book outlines the most strategic aspects of post-merger integration, how use advisors throughout the process and, moreover, the core competencies required for successful M&A programs. Ken Smith has practiced as a strategy consultant for 25 years, earlier with McKinsey and Company and more recently with SECOR Consulting, where he was also a Managing Partner and Chair. In 2010, Ken accepted an appointment at the University of Guelph as an Associate Professor and Associate Dean of Executive Programs in the College of Management and Economics, where he continues research and consulting on business and public policy matters associated with industry restructuring and M&A. Alexandra Reed Lajoux, chief knowledge officer, National Association of Corporate Directors, has three decades of experience in business information. After teaching literature and languages at the university level, she served as editor of a series of professional publications, including Directors & Boards, Mergers & Acquisitions, Export Today, HR Director/Human Capital, and Director's Monthly.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.036
Threshold uncertainty score0.120

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0030.003
Scholarly communication0.0100.008
Open science0.0020.002
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0360.032

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.

Opus teacher head0.015
GPT teacher head0.283
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

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Citations0
Published2012
Admission routes1
Has abstractyes

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