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Record W1598901840

Teaching Chinese R&D Managers

2007· article· en· W1598901840 on OpenAlexaboutno aff
Tom Clarke

Bibliographic record

VenueResearch-Technology Management · 2007
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Knowledge Management
Canadian institutionsnot available
Fundersnot available
KeywordsChinaSurpriseMandarin ChinesePsychologyHistoryLinguisticsSociologyPolitical scienceSocial psychologyLaw
DOInot available

Abstract

fetched live from OpenAlex

Letter from Shanghai The culture that defines effective and ineffective R&D management practices is universal and to a great degree independent of country culture, at least as far as managing R&D in China is concerned. I base this conclusion on my recent experience in teaching four groups of industrial R&D managers in Shanghai, China, in December 2005 and June 2006. When I was first approached to put on a workshop, I had several concerns. The main one was how much of the R&D management best practices that I teach in North America would be applicable in the Chinese culture. Another was the language barrier. I don't speak or understand Mandarin. Thus it was with some trepidation that I accepted the invitation to teach. The language barrier did not prove as difficult as I thought it might. A majority of the workshop participants had a working knowledge of English and I was provided with two-way simultaneous interpretation. Thus when participants were asked to post on flipcharts the results of their group discussions in response to the open-ended questions posed during the workshop, it was not uncommon for the answers to be written in English. The biggest surprise, however, was the answers to those questions. To a great degree, they were identical to the responses I hear from my American and Canadian participants. After a while, I had to keep reminding myself that I was in China, not North America. Thus my primary concern over the transferability of North American/Western European R&D management best practices to China did not prove to be a problem. The following are some of the flipchart questions and answers from the Chinese R&D managers: What gives you the greatest satisfaction on the job? * Having a new product you worked on be successful in the market place. * Being rewarded for working hard. * Receiving recognition and respect from leaders and subordinates. * Receiving promotions and salary increases. * Doing a job you like very much. * Making friends at work-having good relations with co-workers. * Having the right to make decisions. These responses more or less match the answers to a 1999 survey by R&D Magazine on the satisfying aspects of a researcher's job, which they reported to be: * Solving challenging problems. * Quality of the people they work with. * Doing interesting work. * Doing what they are good at. * Ability to work independently. What are the factors that discourage idea generation and creativity? * Organization is too bureaucratic, with a strict hierarchy. * Not enough recognition for the development of intellectual property. * Top management focuses too much on key performance indicators that don't take creativity into account. * People are not given enough time to be creative due to work load. * Company focuses too much on short-term return on investment. * Culture is to always say yes to the boss when the answer should be no. * Fear of failure is too high. * Customers prefer safe solutions, not creative ones. * Motivation system does not support creativity. These answers are clearly in line with the many research studies conducted in North America and Western Europe on what inhibits creativity. The only distinct difference is the difficulty that many Asians have in saying no, when no is the appropriate response to a request to do something. Describe the most effective R&D manager you ever had who motivated you to work to the best of your ability. * Was a good role model. * Was involved, appreciated good work and provided rewards. * Was fair, and open in his dealings with subordinates. * Understood the nature of R&D work (e.g., risky, uncertain). * Fought for subordinates' benefits and compensation. …

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)0.004
metaresearch head score (Gemma)0.005
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: Empirical · Consensus signal: none
Teacher disagreement score0.041
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0070.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0230.005

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 head0.033
GPT teacher head0.348
Teacher spread0.315 · 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

Citations0
Published2007
Admission routes1
Has abstractyes

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