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Record W2340843256 · doi:10.1108/tqm-12-2013-0147

Operational excellence through business process orientation

2016· article· en· W2340843256 on OpenAlexaff
Bahar Movahedi, Kayvan Miri‐Lavassani, Uma Kumar

Bibliographic record

VenueThe TQM Journal · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Knowledge Management
Canadian institutionsCarleton University
Fundersnot available
KeywordsExcellenceBusinessProcess managementProcess (computing)Orientation (vector space)Operational excellenceOperations managementComputer scienceEconomicsPolitical scienceMathematics

Abstract

fetched live from OpenAlex

Abstract Purpose The purpose of this paper is to investigate the role of Business Process Orientation (BPO) at intra- and inter-organizational levels in various aspects of organizational performance. Design/methodology/approach A comprehensive research model is developed based on the review of literature. The research model is later examined and modified based on the analysis of a sample of 3200 for-profit organizations. Factor analysis and structural equation modeling techniques are used to investigate the research questions presented in the study. Findings The findings of this study suggest that while higher levels of BPO at intra-organizational level provide direct financial and operational benefits for the firms in our sample. Higher levels of BPO at inter-organizational level provide direct customer satisfaction benefits as well as indirect financial and operational benefits. Research limitations/implications Lack of sufficient previous studies and theories in this area is one of the main limitations of the study. Practical implications One of the important managerial implications of the present study is that organizations can learn about the types of benefits that they may expect to gain through a higher level of BPO at each of the two levels of analysis in this study: intra- and inter-organizational. Originality/value The measurement models and the comprehensive structural model are the main original contributions of this study. As far as can be determined, this is the first study that measures BPO at intra- and inter-organizational levels with respect to suppliers and customers, in addition to investigating the role of BPO on various aspects of organizational performance indicators through a large scale empirical study.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0070.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.025
GPT teacher head0.260
Teacher spread0.235 · 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 source (direct Gemma or distilled Codex), 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

Citations17
Published2016
Admission routes1
Has abstractyes

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