Citizen behaviors, enterprise social media and firm performance
Bibliographic record
Abstract
Purpose The goal of this study is twofold: first, it seeks to investigate how enterprise social media (ESM) usage contributes to firm performance, especially through operational performance metrics; second, to identify the ESM users’ behaviors that help to improve firm performance. Design/methodology/approach An interpretive case study of a medium-sized manufacturing company in the food industry. After developing a theoretical framework, an exploratory research was undertaken about the use of an ESM. Qualitative methods were adopted for data collection and analytic induction for data analysis, using structural and descriptive coding. A series of semi-structured interviews with senior managers and middle-managers were conducted. Operations performance metrics were also assessed through documentary analysis before and after the implementation of the ESM. Findings The study integrates concepts and theories from across three main fields of research, namely organizational behaviors, management and information systems. It complements the extant research on ESM by providing a new theoretical framework that connects ESM use with firm performance. Empirical findings suggest that ESM contributes to firm performance through social capital development fostered by organizational citizenship behaviors. The emergence of leadership development has been also observed. Research limitations/implications The exploratory nature of the study combined with the fact that it has been conducted within a single organization greatly limits the generalization of the findings. Practical implications Managers can use the findings of this study as a support of a successful ESM implementation. Besides, it provides references for practitioners aiming to use and evaluate ESM and their corresponding citizenship behaviors within a manufacturing milieu. Originality/value The paper is the first to bring a multi-disciplinary perspective of the contribution of ESM usage on firm performance-based in a social capital enacted by organizational citizenship behaviors. These understandings add new insights to the literature and establish new theoretical connections between organizational citizenship behaviors, ESM use and social capital that also allowed to emerge leadership development.
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 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".