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Record W4387003847 · doi:10.18280/ijsdp.180905

Innovative Responses to Exogenous Shocks in Indonesian Transportation Firms: Mediating Role of Sustainable Performance and Outcomes

2023· article· en· W4387003847 on OpenAlexvenueno aff
Abdullah M. Al‐Ansi, Manar Hazaimeh, Aseel Hendi, Jebril AL-hrinat, Ghadeer Adwan, Askar Garad

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsnot available
Fundersnot available
KeywordsIndonesianBusinessSustainable developmentIndustrial organizationEnvironmental economicsEconomicsPolitical science

Abstract

fetched live from OpenAlex

Innovative responsiveness to exogenous shocks for sustainability is a proactive methodology for mitigating and adapting to unforeseen events and challenges that could jeopardize the sustainability of ecosystems or organizations.This research aims to examine three innovative strategies-minimizing impact, shifting approaches, and short-long term planning-for responding to exogenous shocks in two Indonesian private sector transportation firms, Gojek and Grab, and the influence of this response on sustainable development.In addition, sustainable performance and outcomes are employed as mediators between the innovative response and sustainable development.A total of 385 drivers from Gojek and Grab in Yogyakarta, Indonesia, were enlisted to answer questionnaires.The survey was distributed online via social media applications, and the collected data was analyzed using SEM-PLS 4. The findings indicate that the innovative response strategies to exogenous shocks-including minimizing impact, shifting to new approaches, and short-and long-term planning-were effective and contributed to the sustainable development of both companies.The results also suggest that minimizing impact has a negative but insignificant impact on sustainable outcomes, whereas both sustainable performance and outcome positively enhance the sustainable development of both firms.These research findings contribute to the literature by addressing a gap related to sustainability during crises in the Indonesian context.They also have practical implications by raising awareness of innovative, technology-based solutions.

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 score0.065
Threshold uncertainty score0.411

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
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 head0.011
GPT teacher head0.242
Teacher spread0.231 · 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

Citations16
Published2023
Admission routes1
Has abstractyes

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