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Record W3134729067

Investigating the technical and scale efficiency of cement companies in Saudi Arabia

2020· article· en· W3134729067 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueManagement Science Letters · 2020
Typearticle
Languageen
FieldDecision Sciences
TopicEfficiency Analysis Using DEA
Canadian institutionsnot available
Fundersnot available
KeywordsData envelopment analysisBoomCementScale (ratio)BusinessOperations managementIndustrial organizationEconomicsEngineeringMathematicsStatistics
DOInot available

Abstract

fetched live from OpenAlex

Cement & material sector is instrumental in infrastructural development of any economy. The same holds about Saudi cement sector which has contributed substantially to the economic and construc- tion boom in Kingdom of Saudi Arabia (KSA). The cement sector of KSA holds the highest place among other GCC countries. Though, during the last couple of years, the sector seems to be grap- pled with capacity and weighted down due to certain reasons. Nevertheless, almost all of the cement companies in KSA are underplaying their actual capacities. Still, plenty of untapped growth oppor- tunities for cement sector are available in KSA and other GCC countries. Henceforth, considering the growth potential and taking cue from the current scenario of KSA cement sector, the current study endeavors to measure the efficiency of listed cement companies in KSA. The study endeavors to be engrossed in identifying a set of companies which plays on efficiency frontier. Therefore, the technical efficiency performance of fourteen listed cement companies in KSA was measured using Data Envelopment Analysis (DEA) methodology. Two basic models of DEA methodology (i.e. CRS & VRS) were used to estimate the pure and technical efficiency of identified DMUs over a period of four years from 2016 to 2019. The study reveals that over a period of four years and on an average efficiency scale, only 23% of the firms were purely technically efficient on a CRS scale, while 46% of the firms were technically efficient. Only 23% of the firms were scale and technically efficient. Though, companies in the sector have a vast potential to outperform on efficiency front. Yet, the overall efficiency level among Saudi cement companies are remained depressing. Moreo- ver, the study has noticed that the companies which are inefficient did not have a considerable distance from the efficiency frontier. The study also provided significant insights on the input fac- tors causing inefficiency and suggestion to achieve the total technical efficiency. Furthermore, the efficiency analysis also provided benchmarking firms, which are efficient under several criteria for others to imitate their best practices for becoming a significant player on efficiency frontier.

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.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.194
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.006
Science and technology studies0.0000.003
Scholarly communication0.0000.000
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 head0.059
GPT teacher head0.323
Teacher spread0.264 · 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