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Enregistrement W130350733

Human Resource Issues in Global Entrepreneurial High Technology Firms: Do They Differ?

2008· article· en· W130350733 sur OpenAlexaboutno aff
M. Ronald Buckley, Shawn M. Carraher, Sarah C. Carraher, Gerald R. Ferris, Charles E. Carraher

Notice bibliographique

RevueThe Journal of Applied Management and Entrepreneurship · 2008
Typearticle
Langueen
DomaineBusiness, Management and Accounting
ThématiqueInnovation and Knowledge Management
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésHuman resourcesSet (abstract data type)BusinessHuman resource managementPublic relationsInformation technologyMarketingKnowledge managementManagementPolitical scienceEconomicsComputer science
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Executive Summary In spite of the fact that high technology organizations are consistently rated as excellent organizations for which to be employed, we know relatively little about the human resources management practices that have been instrumental in facilitating this belief. In this study we examine the concerns of human resource managers from four samples (one domestic and three global) about their concerns with respect to managerial and professional employees. We conclude that more information is needed specifically geared toward this group of employees in this type of environment and that organizations need to spend more time seeking to accurately assess the performance of professional and managerial employees. Surprisingly little has changed in their practices since Sept. 11, 2001. Although a majority of the positions in high technology organizations could be classified as blue-collar, there has recently been a dramatic increase in the attention given to human resources management issues in high technology organizations as they pertain to the professional (e.g., scientists, engineers, and RD Sullivan, 1999). Additionally, work in entrepreneurial high technology organizations is considerably different than that in any of the other environments in which professionals and managers typically practice (Carraher, Franklin, Parnell, & Sullivan, 2006; Miner & Smith, 1994; Pool, Parnell, Spillan, Carraher & Lester, 2006). The external and internal environments in which high technology organizations exist are neither well defined, nor are they well understood which can create a set of unique demands on the activities of both supervisors and those supervised (Eisenhardt, 1989). In fact, the environment in high technology organizations results in a fundamental dilemma for management practitioners because there is a need to be both structured (in terms of making timely decisions concerning rapidly changing technology), and flexible (able to shift rapidly due to changes in technology). We must conclude, then, that surprisingly little is known about high technology organizations, and that what we believe to be true may well be based upon a number of misunderstandings and assumptions about the transferability of generic management practices to high technology environments (Ferris, Hockwarter, Buckley, Harrell-Cook, & Frink, 1999). The purpose of this paper is to look at several areas of human resource management where processes in high technology environments may differ from other environments and to examine these issues as they pertain to the management of human resources in multinational entrepreneurial high technology organizations. This paper is an extention of the work of Buckley, Carraher, Ferris, and Carraher (2001) with data from after Sept. 11, 2001. Methods Samples In order to shed some light on issues of interest to multinational entrepreneurial high technology organizations, we solicited the responses of the human resources directors of three groups of high technology organizations [all groups of firms focus on computer hardware and software]. For sample 1 (domestic firms) we surveyed 104 human resource directors from the Midwest in the late 1980's. For sample 2 (multinational firms) we surveyed 318 human resource directors attending 3 technology conferences 10 years later. For sample 3 we surveyed 155 human resource directors in late 2001. For sample 4 we surveyed 138 of the human resource directors in early 2006. The organizations represented were from (according to the numbers in the sample) the U.S.A., Japan, Canada, South Korea, Mexico, China, Taiwan, and Malaysia and had been in existence from 2 weeks to over 100 years with over 56% having been created within the last 10 years. Data Collection The first group of organizations was sampled via surveys mailed through the U. …

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,008
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,006
Score d'incertitude au seuil0,020

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0030,008
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0020,004
Études des sciences et des technologies0,0010,002
Communication savante0,0050,002
Science ouverte0,0000,002
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0060,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,014
Tête enseignante GPT0,223
Écart entre enseignants0,209 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations9
Publié2008
Routes d'admission1
Résumé présentoui

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