MétaCan
Menu
Back to cohort
Record W130350733

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

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

Bibliographic record

VenueThe Journal of Applied Management and Entrepreneurship · 2008
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Knowledge Management
Canadian institutionsnot available
Fundersnot available
KeywordsHuman resourcesSet (abstract data type)BusinessHuman resource managementPublic relationsInformation technologyMarketingKnowledge managementManagementPolitical scienceEconomicsComputer science
DOInot available

Abstract

fetched live from 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. …

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.285
Threshold uncertainty score0.861

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.014
GPT teacher head0.223
Teacher spread0.209 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations9
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of Applied Management and EntrepreneurshipSame topicInnovation and Knowledge ManagementFrench-language works237,207