Enterprise social media platforms for coping with an accelerated digital transformation
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".