Human Resource Issues in Global Entrepreneurial High Technology Firms: Do They Differ?
Bibliographic record
Abstract
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. …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".