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Record W4283212557 · doi:10.1108/jsit-08-2021-0154

Enterprise social media platforms for coping with an accelerated digital transformation

2022· article· en· W4283212557 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.

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

Bibliographic record

VenueJournal of Systems and Information Technology · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicKnowledge Management and Sharing
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsDigital transformationSocial mediaKnowledge managementExploratory researchContext (archaeology)Social media analyticsComputer scienceProcess managementBusinessSociologyWorld Wide Web

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to investigate how to support small organizations to navigate the context of an accelerated Digital Transformation using Enterprise Social Media platforms, in response to external contingencies, such as the COVID-19 pandemic. Design/methodology/approach A longitudinal action research study, supported by an exploratory analysis that follows a hybrid approach of deductive and inductive reasoning, has been conducted in the context of a small organization. Several data collection techniques were used for context understanding and problem-solving. Findings Findings suggest that value creation related to the use of Enterprise Social Media platforms supports small organizations in this accelerated context of Digital Transformation. Value perception is central in overcoming adoption barriers and achieving sustainable use of these platforms in daily basis activities, especially in remote working. External pressures, like those imposed by the COVID-19 pandemic, play an important role in catalyzing digital initiatives. Research limitations/implications As the main limitations to this paper, we highlight the study of a single organization in a specific context and the number of actors involved; hence, there is room to extend the study to other industries, organization sizes and contexts. Practical implications This paper provides managers with insights into how to conduct their Enterprise Social Media initiatives in a turbulent environment, highlighting their key success elements, and their potential to create value for their organizations and stakeholders. Furthermore, managers could explore the potential of Enterprise Social Media platforms to support organizations in the Digital Transformation journey. Social implications Small organizations play an important role in generating wealth for nations around the world. However, governments encounter difficulties in supporting the Digital Transformation of this type of organization. This paper provides insights into how to use an affordable and intuitive technology to include this type of organization in the Digital Transformation journey. Originality/value A long-term study of Enterprise Social Media is recommended, but quite rare in the Information Systems literature. This study adopts a longitudinal investigation to analyze the use of Enterprise Social Media to support a small organization to adapt, in balance with their internal and external contingencies, providing a further contribution to the contingency theory. This research also adds contributions to the sociotechnical system perspective, analyzing the deep imbrication between social and technical subsystems in the required organizational change, supporting a small organization for coping with the effects of the COVID-19 pandemic.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.601
Threshold uncertainty score0.502

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.005
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 head0.024
GPT teacher head0.267
Teacher spread0.243 · 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