The dark side of hyperconnectivity in the accounting profession
Bibliographic record
Abstract
Purpose This paper aims to understand the antecedents and consequences of using the smartphone and/or tablet by professional accountants for business purposes outside of regular working hours. More specifically, this paper aims to test the direct relationships between, on the one hand, work intensification and the use of smartphone and/or tablet and work-family conflict (WFC) and on the other hand, the indirect effect of the use of smartphones and/or tablet between work intensification and WFC. Design/methodology/approach This research was based on a cross-sectional design and quantitative method. The structural equation method was used to test the direct effect of work intensification on smartphone and/or tablet use and WFC. As for the mediation effect of smartphone and/or tablet use between work intensification and WFC, it was tested by the method of indirect effects based on a bootstrap analysis. The statistical treatments were carried out with the AMOS software v.24. Findings The results of the study indicate that work intensification increases the use of smartphones and/or tablets outside of working hours and that this variable increases the intensification of WFC through a process of mediation. Research limitations/implications This research does not take into account the moderating variables that can intervene in the model. For example, the duration of use of the smartphone, the origin of emails or messages (supervisors, customers and colleagues), the types of tasks performed outside working hours and the period of use (evening, weekend and holidays) could have significant effects on the different relationships tested in the model. Furthermore, we had all the positions held by the respondents (for example, chief financial officer, director, vice-president, partners, senior managers, management accountant, auditor, agents, analysts, accountants) grouped into one category and this may have an impact on results. Practical implications The results could be quite interesting for governments and organizations interested in advantage of the technology while reducing its disadvantages. In particular, it is important for accounting firms, which are big users of new technologies (e.g. Smart software and analytics). Indeed, both companies and professional accountants must clearly communicate their expectations regarding the use of technology for business purposes outside normal working hours. Originality/value To the best of the authors’ knowledge, this study is the first to examine the effect of work intensification and the use of smartphones and/or tablets, on WFC.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".