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Record W3145092376 · doi:10.1108/ijlm-12-2020-0486

Stakeholders conflict and private–public partnership chain (PPPC): supply chain of perishable product

2021· article· en· W3145092376 on OpenAlexaff
Mahmud Akhter Shareef, Yogesh K. Dwivedi, Jashim Uddin Ahmed, Uma Kumar, Rafeed Mahmud

Bibliographic record

VenueThe International Journal of Logistics Management · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSupply Chain Resilience and Risk Management
Canadian institutionsUniversity of OttawaCarleton University
FundersBangladesh Fisheries Research Institute
KeywordsSupply chainBusinessGeneral partnershipInformation sharingSupply chain managementContext (archaeology)ProcurementMarketingGovernment (linguistics)Process managementIndustrial organizationComputer scienceFinance

Abstract

fetched live from OpenAlex

Purpose This paper aims to address procurement, logistics management, inventory control and distribution of perishable items, i.e. vegetables, fruits, flowers and fishes, during the social isolation period of the Covid-19 era to identify conflicting interests among the channel members; present inventory and information sharing scenario; and reveal organizational dispute and existence of redundant, nonessential and corrupted members in the supply chain. Design/methodology/approach This study uses an exploratory investigation to evaluate the relations among the members of the supply chain of perishable food items. In this context, it is designed to investigate the field, observe the members of the existing supply chain from rural and remote places and capture their interviews to accomplish the objectives. Findings This study identified that although the supply chain of perishable food items is controlled truly by private parties, from a realistic view, the private–public partnership is essential where the government should play the coordinating role. In this context, continuous interaction, coordination and information sharing among the members to establish an optimum and scalable network and remove any redundant nodal points is a key success factor for managing an efficient supply chain. Research limitations/implications Theoretical and managerial implication of this research is enormous. The existence of functional and dysfunctional conflicts in the same supply network and how it can be detrimental to the performance of the members are exposed in this study, which can be an excellent source to be investigated. Practitioners and researchers can gain a greater understanding to identify the root causes of conflicts in the existing structural dynamics, shedding light on organizational interactions, power and group behavior during the Covid-19 era. Originality/value From the light of management and inter-organizational conflicts, this is a pioneer study that has detected the redundant channel members, their source of power and how their removal can present an optimum channel with group coherence and synergistic interest.

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)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.004
Scholarly communication0.0050.005
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.084
GPT teacher head0.276
Teacher spread0.192 · 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 designQualitative
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

Citations19
Published2021
Admission routes1
Has abstractyes

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