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Record W1522916122 · doi:10.4337/9781847204134.00024

Women Advancing onto the Corporate Board

2007· book-chapter· en· W1522916122 on OpenAlexaboutno aff
Val Singh, S Vinnicombe, Siri Terjesen

Bibliographic record

VenueEdward Elgar Publishing eBooks · 2007
Typebook-chapter
Languageen
FieldSocial Sciences
TopicGender Diversity and Inequality
Canadian institutionsnot available
Fundersnot available
KeywordsPaceCorporate governancePoliticsGender diversityRepresentation (politics)CommissionChinaDiversity (politics)Political scienceBusinessAccountingGeographyLawFinance

Abstract

fetched live from OpenAlex

In the last decade, there has been increasing awareness of the slow pace of advancement of women onto corporate boards, despite over thirty years of equal opportunities policies. The lack of female representation in corporate decision-making is now an important issue for policy-makers, particularly in Scandinavia where political intervention is underway. Gender diversity on corporate boards is an emergent issue for developing economies such as India and China, and some countries in the Middle East (Tunisia, Jordan, Egypt and Morocco) are also starting to recognise the importance of developing their female talent up to board level. Indeed, until recently, the lack of women on top corporate boards appeared to be a global phenomenon, with women constituting less than 15 per cent of members of top company boards in the USA, the UK, Canada, Australia, New Zealand and many European countries. However by 2005, Norway, Sweden, Slovenia, Estonia, Bulgaria, Romania and Finland had at least 15 per cent female representation on their top 50 corporate boards (European Commission, 2005). In this chapter, we consider theoretical perspectives that shed light upon the persistence of this phenomenon and how positive change can be achieved. We examine the international statistics on women directors, including those from Scandinavia where quota systems have recently been introduced. We then consider the characteristics of companies that have appointed women directors. This is followed by an examination of the characteristics of women directors on large corporate boards, including their human capital. We then consider the links between women on boards and corporate performance, reviewing extant research on the business case, the relationship between gender diversity on corporate boards and firm financial performance, as well as the link with good corporate governance. Highlighting the approaches selected by the USA, UK and Scandinavia, we consider next how different countries have addressed the issue of lack of female representation on corporate boards. We report a new mentoring scheme in the UK involving top chairmen and senior women managers in non-competing companies. We conclude with suggestions for further research. Before we begin, we should clarify the terms for the different types of directors and boards. In the US, the term for a corporate board director with executive responsibility and an employment contract with the firm is 'inside director.' In the UK, such directors are 'executive directors' (ED), but do not include the company secretary, the legal officer who is generally considered an inside director in the USA. Similarly, the American term 'outside director' is equivalent to 'non-executive director' (NED) in the UK, and 'supervisory board director' in other parts of Europe. In the US and UK, single tier boards comprise both inside and outside directors, including both chairman and chief executive, all with legal status as directors. In most European governance systems, there is a two-tier board, with the chief executive running the executive board (whose members do not have legal status) and the chairman running the supervisory board of directors.

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)0.004
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: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0040.002
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0170.002

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 head0.146
GPT teacher head0.278
Teacher spread0.132 · 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

Citations31
Published2007
Admission routes1
Has abstractyes

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