The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Employee Attitudes and (Digital) Collaboration Data: A Preliminary Analysis in the HRM Field
Open accessAbstract
The digital transformation of organizations is making workplace collaboration more and more powerful and work always "observable"; however, the informational and managerial potential of the generated data is still largely unutilized in Human Resource Management (HRM). Our research, conducted in collaboration with business engineers and economists, aims at exploring the relationship between digital work behaviors and employee attitudes. This paper is a work-in-progress contribution that presents a preliminary phase of data analysis we performed on a collection of Enterprise Collaboration Software (ECS) data. In the exploratory data analysis step, we analyze data in their original table format and elaborate it according to the user who performed the action and the performed action. Then, we move to a graph representation in order to make explicit the interaction between users and the objects of their actions. Finally, we introduce the concept of employee-attitude-oriented pattern as a mean to derive significant views over the overall graph and discuss Social Network Analysis (SNA) approaches that can be exploited for our purposes.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
An evidence-based review of HR Analytics
Marler, J. H., & Boudreau, J. W. (2017) · The International Journal of Human Resource Management · doi:10.1080/09585192.2016.1244699 · Cited by 913
Research works printed but not linked: 18
- 10.1002/job.580 · DOI
- 10.1016/j.jvb.2005.08.003 · DOI
- 10.1016/j.biosystems.2014.11.005 · DOI
- 10.1016/j.ijinfomgt.2017.08.003 · DOI
- fabbri, 2018, Framing employees’ attitudes and digital work behaviors to support data-driven human resource management: an exploratory study, 7th International e-HRM Conference
- 10.1145/3155897 · DOI
- 10.14778/2732286.2732289 · DOI
- 10.1145/1835804.1835934 · DOI
- 10.1016/j.eswa.2017.05.028 · DOI
- 10.1145/2736277.2741661 · DOI
- fabbri, 2018, Digitalization and hr analytics: a big game for an hr manager, Human Resource Management and Digitalization
- 10.1016/j.hrmr.2016.08.005 · DOI
- fueller, 2015, What makes great salespeople, Harvard Bus Rev
- 10.2307/20159613 · DOI
- mitchell, 2001, Why people stay: Using job embeddedness to predict voluntary turnover, Academy of Management Journal, 44, 1102, 10.2307/3069391 · DOI
- 10.1007/978-3-319-77329-2_3 · DOI
- barnard, 1938, The Functions of the Executive
- 10.1016/j.jss.2015.07.028 · DOI
Cited by
Sorted by citations
Exploring Human Resource Management Digital Transformation in the Digital Age
Zhang, J., & Chen, Z. (2024) · Journal of the Knowledge Economy · doi:10.1007/s13132-023-01214-y · Cited by 414
“There are at least two relevant results for the digital management of the organization (Fabbri et al., 2019).”
Background
Metadata
- Authors
- Fabbri, T., Mandreoli, F., Martoglia, R., & Scapolan, A. C.
- Affiliations
- University of Modena and Reggio Emilia (IT)
- Venue
- 2019 28th International Conference on Computer Communication and Networks (ICCCN)
- Publisher
- IEEE
- Published
- 2019-07
- Pages
- 1-6
- ISSN
- 1095-2055 (ISSN-L)
- OpenAlex
- W2976546903
- Cited by
- 20
- References
- 1
- Citations by year
- 2025: 4 · 2024: 3 · 2023: 3 · 2022: 5 · 2021: 4 · 2020: 1
- FWCI
- 11.53
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- International Conference on Computer Communications and Networks (ICCCN) (publishedVersion)
- IRIS UNIMORE (University of Modena and Reggio Emilia) (submittedVersion, cc-by)
- Topics
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Data Mining
- Social Sciences › Decision Sciences › Information Systems and Management › Knowledge Management and Sharing
- Keywords
- employee attitudes, workplace collaboration, enterprise collaboration software, digital transformation, user interaction networks, social network analysis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Employee Attitudes and (Digital) Collaboration Data: A Preliminary Analysis in the HRM Field. https://app.cochat.ai/papers/10.1109/icccn.2019.8846957
- The paper
- Fabbri, T., Mandreoli, F., Martoglia, R., & Scapolan, A. C. (2019). Employee Attitudes and (Digital) Collaboration Data: A Preliminary Analysis in the HRM Field. 2019 28th International Conference on Computer Communication and Networks (ICCCN). https://doi.org/10.1109/icccn.2019.8846957