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Record W1599719915

Information Technology Management: A critical Analysis of Managerial Impediments Facing Information Technology Managers

2011· article· en· W1599719915 on OpenAlexaffabout
Vincent Sabourin

Bibliographic record

VenueComputer Engineering and Intelligent Systems · 2011
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInformation Technology Governance and Strategy
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsObstacleAction (physics)PerceptionDimension (graph theory)Knowledge managementBusinessProcess managementComputer sciencePublic relationsPsychology
DOInot available

Abstract

fetched live from OpenAlex

The paper intended to study managerial impediments which may hinder strategy and objective implementation by IT managers and technicians. The managerial drivers included: rules, initiatives, emotions, immediate action and integrity. This paper describes the drivers of strategy implementation by managers in IT departments to implement their organizational objectives. The findings on Perception of IT managers and administrators towards the managerial drivers of objective implementation by IT managers has put a lot of emphasis on rules and the lack of commitment of employees (the dimension of emotions) to explain the obstacles faced by IT managers. Though the finding of our data suggests that a driver of emotions is the most critical obstacle to IT management, there are important drivers, such as immediate action that will force managers to take emergencies to deal with urgent matters without compromising organizational objectives. Thus it also proves to be vital driver to IT management. Purpose: This research was carried out to investigate on the strategic impediments facing IT managers with regard to their attitudes and organizational perception. The study involved effective drivers of management, which constituted individual obstacles that IT administrators and technicians face during their objective implementation. Methodology: A mixed method of qualitative (focus group discussion) and quantitative (a survey with a questionnaire) approaches was applied to this study. These involved group discussion of IT technicians and administrators in the selected organizations in a Canadian province. The total number of surveyed managers was 147. Results : With regards to the drivers of management, it was established that the driver of emotions holds the highest consideration towards attitudes, management and employee motivation. This driver had, a frequency recorded 137, mean of 3.6923, median of 3.800 and standard deviation of 0.81950. The driver of rules was after analysis found to have a frequency of 145, a mean of 3.107, median of 3.400 and standard deviation of 0.76265. The driver of immediate action had a frequency of 134, mean of 3.1448, median of 3.200 and standard deviation of 0.86135. The driver of integrity had a frequency of 134, mean of 3.0489, median of 3.00 and standard deviation of 0.90948. The driver of initiatives had a frequency of 138; mean score of 3.4214, median of 3.45 and standard deviation of 0.85542. The summary of the report has been presents in table 2. Conclusion: This study is focused on the impediments experienced by IT managers as they implement their objectives. Taken as a whole, our findings suggest that, there are some impediments associated with drivers of Emotions, immediate action, Rules and initiatives. Even if these obstacles are in multiple levels to develop and promote IT management and objective implementation skills, it is imperative to study with more depth obstacles faced by IT managers in order to better understand how obstacles they face represent an impediment to the development of their competencies and effective performance in IT. Keywords: Information Management, Strategic and Objective implementation, Information technology (IT), Information and Communication Technology (ICT)

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.953
Threshold uncertainty score0.840

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.002
Science and technology studies0.0000.000
Scholarly communication0.0000.003
Open science0.0000.000
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 head0.008
GPT teacher head0.190
Teacher spread0.182 · 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 designSimulation or modeling
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
Published2011
Admission routes2
Has abstractyes

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