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Record W196898945

Budgeting for International Operations: Impact on and Integration with Strategic Planning

2011· article· en· W196898945 on OpenAlexaboutno aff
Juan M. Rivera, Ken Milani

Bibliographic record

VenueManagement accounting quarterly · 2011
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInformation Technology Governance and Strategy
Canadian institutionsnot available
Fundersnot available
KeywordsCurrencyLiberian dollarEconomicsExchange rateInflation (cosmology)Multinational corporationMonetary economicsValue (mathematics)Interest ratePosition (finance)BusinessInternational economicsFinancial economicsFinance
DOInot available

Abstract

fetched live from OpenAlex

Foreign currency exchange rates, interest rates, and inflation are the three major external factors that affect multinationals' budgets. Chief financial officers know they have no influence or control over this Bermuda Triangle of outside forces. Nonetheless, these elements must be estimated, evaluated, and examined as part of a multi national's strategic plan. Although these variables are interrelated (for example, higher inflation in a specific country tends to drive down the value of its currency, which impacts the exchange rate, and price inflation would drive up interest rates), the changes in currency exchange rates have the most direct effect on the budgeting process for a multinational corporation. We will show you 19 company examples so you can see the broad range of issues involved. (Some of the company names are hypothetical.) Example 1 The uncertainty introduced by the volatility of foreign currency exchange rates has been evident in recent years. In the span of one year, the value of the euro went from $1.48 on February 25, 2008, to $1.27 exactly a year later, a decrease of 14%. A similar significant change was observed in other major currencies (e.g., the value of the British pound went from $1.97 to $1.42, and the Canadian dollar depreciated from $1.00 to $0.80 during the 12 months ending on February 25, 2009). The general strengthening of the U.S. dollar was expected to negatively impact the already weak trading position of U.S. exporters. (2) On the other hand, the June 2010 decision of the Chinese government to allow more flexibility to the yuan did provide export opportunities for U.S. companies. (3) Changes in these three external factors stem from several sources, including economic conditions, government policies, monetary systems, and political risks. Each factor is a significant external variable affecting areas such as policy decisions, organizational procedures, and budget control. To minimize the possible negative impact of these factors, multinational corporate management must establish and implement policies and practices that recognize and respond to them. Other external forces exist, such as political turmoil, competition, labor quality, and cultural or religious orientation of the local populace, but they tend to be related specifically to one country or particular region of the world. For example, the events of September 11, 2001, have been significant to U.S.-based multinational corporations. Since 9/11, the strategic plans of many international entities have focused on security measures, employee counseling, and other special training that they had not paid much attention to in the past. All of these efforts must be addressed in budgeting for an international operation. FOREIGN CURRENCY EXCHANGE RATES Of all factors influencing international budgeting, foreign exchange rates have the most significant and pervasive effect. Changes in foreign exchange rates are explained by different theories, but essentially they are based on the underlying demand for assets denominated in a particular currency. Foreign exchange rate fluctuations affect a multinational through translation exposure, transaction exposure, and economic exposure. Each of these exposures has a different effect on the entire budgeting process and on the strategic marketing and operating decisions. Translation Exposure Translation exposure influences financial statements during the development of a budget and/or while the budget is being used for control purposes. Specific exchange rates, usually based on forecasted values, must be determined and applied when preparing the budgeted financial statements from the applicable operations budgets. (4) Throughout the budgetary period, the actual exchange rates likely will vary from the anticipated exchange rates. The differences can generate unpredictable--often uncontrollable--results during interim and final budget performance reviews. …

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)0.017
metaresearch head score (Gemma)0.025
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.024
Threshold uncertainty score0.090

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.025
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.007
Science and technology studies0.0030.002
Scholarly communication0.0170.010
Open science0.0010.006
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0240.006

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 head0.023
GPT teacher head0.243
Teacher spread0.220 · 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
GenreEmpirical

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".

Quick stats

Citations7
Published2011
Admission routes1
Has abstractyes

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