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Record W4293214128 · doi:10.17705/1cais.05031

Examining Ecosystems and Infrastructure Perspectives of Platforms: The Case of Small Tourism Service Providers in Indonesia and Rwanda

2022· article· en· W4293214128 on OpenAlexfundno aff
Christopher J. Foster, Caitlin Bentley

Bibliographic record

VenueCommunications of the Association for Information Systems · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Marketing and Social Media
Canadian institutionsnot available
FundersEconomic and Social Research CouncilInternational Development Research Centre
KeywordsTourismBusinessService providerValue (mathematics)Goods and servicesService (business)MarketingCommerceEconomicsEconomyGeographyComputer science

Abstract

fetched live from OpenAlex

Digital platforms are significantly affecting how firms and individuals undertake economic exchange. With their global expansion, exploring the implications of platforms for those who sell goods or provide services in the global south is an important agenda for determining their value. Yet, we argue that existing frameworks only provide a partial understanding of activities and relations. In this paper, we examine platforms through an analysis of two theoretical perspectives. Established ecosystems perspectives focus on platform governance, centralizing the activities of the ‘platform owner’. Such perspectives allow an analysis of platform strategy but can underplay the ways platform sellers and service providers engage with platforms. Infrastructure perspectives, in contrast, approach platforms as large and complex systems, which we argue allows for better analysis of the practices and agency of such actors. An analysis of small tourism service providers in Indonesian and Rwandan tourism supports the discussion of these two perspectives. Findings highlight the growth of global platforms, but service providers face challenges in using them effectively. Infrastructure perspectives highlight risks that service providers face in being pulled into adverse relationships as platforms become ubiquitous. As platforms expand, their complexity leads to challenges in engagement, but with potential for learning and collaboration.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.241
Threshold uncertainty score0.618

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.024
GPT teacher head0.261
Teacher spread0.238 · 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 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

Citations8
Published2022
Admission routes1
Has abstractyes

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