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Record W2917276316 · doi:10.1108/00197851211267956

Transferring corporate knowledge via succession planning: analysis and solutions – Part 2

2012· article· en· W2917276316 on OpenAlexaff
Steven H. Appelbaum, Christina Benyo, Heidi Gunkel, Said Ramadan, Fadi Sakkal, Damian Wolff

Bibliographic record

VenueIndustrial and Commercial Training · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicHuman Resource and Talent Management
Canadian institutionsConcordia University
Fundersnot available
KeywordsSuccession planningRestructuringWorkforceProductivityMarketingJob satisfactionDemographicsBusinessTest (biology)Employee moraleKnowledge managementPublic relationsPsychologyManagementEconomicsSociologyComputer sciencePolitical scienceEngineering

Abstract

fetched live from OpenAlex

Purpose The focus of the paper is a large, national, publicly traded company. As a large number of its workforce consists of “baby boomers”, 50 percent of its employees will be of retirement age within the next five years. Having acknowledged the company's concerns about efficiency of new employees and the loss of morale in senior employees, this two‐part article sets out to provide motivational tools for management and to suggest solutions to restructure and reorganize in a way to prevent the inevitable – i.e. the loss of corporate knowledge – via succession planning. Design/methodology/approach A survey and individual interviews were conducted within the company to measure current job satisfaction and company culture as well as how the different generations of employees view each other and themselves. Employees were also questioned about their legacy in the organization and their thoughts about transferring corporate knowledge from one generation to another. This is a two‐part article. Part 1 covers the background to the case under investigation, a review of relevant literature and the hypotheses to test the problem to be studied and solved. Part 2 describes the methodology and data related to demographics, the testing of the hypotheses and conclusions and recommendations. Findings The study found a correlation between job satisfaction and effective communication and a negative correlation between pre‐retirees and their lack of motivation as they approach retirement. Also, it was found that senior employees possess a willingness to share and transfer knowledge to younger generations. Research limitations/implications The article relies on limited survey and interview data of one particular regional department within a large organization. Certain demographic questions were omitted to preserve confidentiality. The selected department and interview subjects were chosen by the organization and therefore the occurrence of sampling error is possible. Practical implications This study monitored the impact that recent retirees will have on the remaining staff of a company. The act of effective succession planning is of paramount importance since the “baby boomer” generation is comprised of 76 million North Americans and the threat of the loss of corporate knowledge will inevitably increase as the population ages. The article offers recommendations as to how an organization can better manage the impact of a large number of retirees on the remaining workforce and what it can do to maximize efficiency. Originality/value The article offers practical solutions for dealing with the impact of retiring baby boomers and identifies models for a proactive approach in dealing with an issue that will affect the North American economy within the next five years.

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)0.003
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 score0.008
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.003
Science and technology studies0.0020.002
Scholarly communication0.0060.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.295
GPT teacher head0.290
Teacher spread0.005 · 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

Citations13
Published2012
Admission routes1
Has abstractyes

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