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Record W3009346654 · doi:10.1108/jbim-05-2019-0173

Exploiting business networks in the age of social media: the use and integration of social media analytics in B2B marketing

2020· article· en· W3009346654 on OpenAlex
Yun Wang, Michel Rod, Qi Deng, Shaobo Ji

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Business and Industrial Marketing · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBig Data and Business Intelligence
Canadian institutionsUniversity of New BrunswickCarleton University
Fundersnot available
KeywordsSMA*Knowledge managementSocial mediaSocial media analyticsAnalyticsBusinessMarketingComputer scienceData science

Abstract

fetched live from OpenAlex

Purpose Based on an organizational capability perspective, this paper aims to propose a development model for social media analytics (SMA) capability that can be applied to business-to-business (B2B) marketing, with the aim of facilitating the use and integration of SMA in B2B marketing and maximizing the benefits of business networks in the age of social media. Design/methodology/approach This is a critical interpretive synthesis of SMA publications collected from academic journals, business magazines and the SMA service industry. In addition, an inter-disciplinary approach was adopted by drawing upon both marketing and information systems literature. In total, 123 academic papers, 106 industry case studies and 141 magazine papers were identified and analyzed. The findings were synthesized and compiled to address the predefined research question. Findings An SMA capability development model is proposed. The proposed model consists of four inter-dependent levels (technological, operational, managed and strategic) that collectively transfer the technological capability of SMA to the dynamic organizational capability. Each level of SMA capability is detailed. SMA-in-B2B marketing is highlighted as a socio-technical phenomenon, in which a technological level SMA capability is emphasized as the foundation for developing organizational level SMA capabilities and organizational capabilities, in turn, supporting and managing SMA activities and practices (e.g. strategic planning, social and cultural changes, skills and resources, measurements and values). Practical implications The proposed research framework may have implications for the operational level SMA development and the investigations on the direct and/or indirect measurements to help firms see the impact of SMA on their business. Originality/value This study may have implications for the adoption, use, integration and management of SMA in B2B marketing. The proposed model is grounded on the integrated insights from academia and industry. It is particularly relevant to B2B firms that have engaged in or plan to engage in applying SMA to extract insights from their online networks and is relevant to B2B researchers who are interested in SMA, big data and information technology organization integration studies.

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)0.019
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.592
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.001
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 head0.177
GPT teacher head0.274
Teacher spread0.097 · 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