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Record W2075177475 · doi:10.5539/ass.v10n2p172

Innovative Work Behavior (IWB) in the Knowledge Intensive Business Services (KIBS) Sector in Malaysia: The Effect of Leader-Member Exchange (LMX) and Social Capital (SC)

2013· article· en· W2075177475 on OpenAlexvenueno aff
Sethela June, Yeoh Khar Kheng

Bibliographic record

VenueAsian Social Science · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessSocial capitalTacit knowledgeContext (archaeology)Perspective (graphical)Order (exchange)Work (physics)MarketingSurvey data collectionWork behaviorHuman capitalProduct (mathematics)Business administrationKnowledge managementEconomicsEconomic growthSociology

Abstract

fetched live from OpenAlex

Today, innovation is no longer belongs to the research and development (R&D) lab per se. In fact, innovation can be considered more of a product of the human creative mind with an inherent tacit knowledge. In order to be a successful knowledge-based economy, employees must continually be innovative. As such, this research which relates to innovation by and large will assist in creating better understanding of innovation from the behavioral perspective. In the context of business, innovation has long been embraced by organizations seeking to remain viable, effective and competitive in a dynamic business environment. Looking at the perspective of individual level innovation, this study seeks to examine whether employees’ innovative work behavior (IWB) can be influenced by leader-member exchange (LMX) and the social capital (SC) in the knowledge intensive business services (KIBS) sector in Malaysia. Using questionnaire mail survey a total of 318 data was obtained from the knowledge workers of the Multimedia Super Corridor (MSC) status companies in Malaysia. The findings show that LMX and SC was significantly and positively related to IWB. The result also reveals that SC has a stronger influence on the IWB of employees as compared to LMX. Discussions and implications of the study are discussed.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.394

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.005
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.015
GPT teacher head0.256
Teacher spread0.241 · 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 designObservational
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

Citations12
Published2013
Admission routes1
Has abstractyes

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