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Record W3097643698 · doi:10.5267/j.msl.2020.10.003

The effect of business continuity management and technology acceptance model towards lecturers’ performance moderated by servant leadership

2020· article· en· W3097643698 on OpenAlexvenueno aff
Herson Keradjaan, Bernhard Tewal, Viktor P. K. Lengkong, Greis M. Sendow

Bibliographic record

VenueManagement Science Letters · 2020
Typearticle
Languageen
FieldComputer Science
TopicOrganizational and Employee Performance
Canadian institutionsnot available
Fundersnot available
KeywordsServant leadershipModerationStructural equation modelingPsychologyTechnology acceptance modelManagementKnowledge managementBusinessLeadership styleComputer scienceSocial psychologyUsabilityHuman–computer interaction

Abstract

fetched live from OpenAlex

This research investigates the effect of business continuity management and technology acceptance models towards performance of lecturers moderated by servant leadership. The research respondents were 86 Halmahera University lecturers. The research method used was quantitative method by examining the effect of moderation and the direct effect by the method of interaction. The model was tested with structural equation modeling approach by using Smart PLS software. The results proved that servant leadership was able to influence lecturer performance. Servant leadership could also moderate the effect of business continuity management towards lecturer performance. However, business continuity management and technology acceptance models did not affect lecturer performance independently. Servant leadership also failed to moderate the effect of the technology acceptance model towards lecturer performance. This study recommends that servant leadership be applied at all levels of leadership because of the very important role in triggering lecturer performance. The application of servant leadership can also be combined with business continuity management since the interaction between of the two can improve lecturer performance.

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: Empirical
Teacher disagreement score0.684
Threshold uncertainty score0.590

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0020.001
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 head0.200
Teacher spread0.186 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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