Technostress in entrepreneurship: focus on entrepreneurs in the developing world
Bibliographic record
Abstract
Purpose This study analyzes technostress in African entrepreneurship. It advances contextualized theoretical explanations of technostress depicting its impact on entrepreneurs who excessively consume digital technology in Africa. The study also describes how research linking transactional benefits to digital technology has created an imbalanced literature that ignores technostress and well-being in African entrepreneurship. Design/methodology/approach Considering the study’s theoretical explanations derived at the technostress–entrepreneurship–well-being nexus, structural equation modeling (SEM) was deemed appropriate. Unlike qualitative–based methods, SEM experiments on 643 observations of early–stage African entrepreneurs in South Africa enabled robust statistical interpretations of their social settings. Thus, strengthening our analysis and focus on the interplay between the variables of technostress, including overload, invasion, complexity and uncertainty, and their impact on entrepreneurship intentions defined through perceived behavior control, entrepreneurship passion and digital self-efficacy. Findings SEM experiments on these African entrepreneurs revealed technostress dimensions of overload, invasion, complexity and uncertainty as moderators of their entrepreneurial actions encompassing perceived behaviour control and entrepreneurship passion in connection with their entrepreneurial intentions. The results also suggested that perceived behaviour control, entrepreneurship passion, and the digital self-efficacy of these entrepreneurs influenced their entrepreneurial intentions. Research limitations/implications Besides inspiring more studies on technostress and well-being in varied entrepreneurial contexts, this research also initiates debate on policy and social reforms geared toward entrepreneurs considered vulnerable to excessive digital technology consumption. Originality/value The novelty of this study lies in its theoretical explanations derived at the technostress–entrepreneurship–well-being nexus. This conceptual overlay elevates the interpretations of the findings of this study beyond the averages in entrepreneurship and information technology (IT) research. Specifically, it increases their inferential value by revealing subtle and hard to dictate social interactions inherent in how African entrepreneurs consume and are impacted by technology as they pursue their entrepreneurial endeavors.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| 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".