Introducción del contexto en la investigación SEW: retos y oportunidades / Introducing context in SEW research: challenges and opportunities
Bibliographic record
Abstract
Based on a sample of family firms from five diverse countries, this empirical exploratory study investigates noneconomic driver represented by socioemotional wealth (SEW) across cultures. SEW refers to the pursuit of affective endowment by family firms and has been recently emphasized to be the main reference point for family firms. Despite the popularity of SEW, there has been concerns regarding its validity as a construct. Since SEW is deeply rooted in family firms; it is thus suggested to be highly influenced by contextual settings, this study suggests that one way to enhance SEW research is by contextualizing it. A random sample was obtained from family firms in Saudi Arabia, Spain, Mexico, Vietnam and Canada through a number of relevant data bases. The data was collected from chairmen and CEOs of firms who were also family members. A multi-group comparison and a linear regression were performed to explore the differences in SEW across cultures as well as the moderating effect of cultures on the relationship between SEW and performance. Findings suggests that SEW varies across cultures. Some SEW dimensions were found to be different across some cultural groups, while the “Emotional Attachment” showed to be consistently different across all cultural groups. The research also shows that culture moderates the relationship between SEW and performance. It was found to be significant and positive in societies with high power distance and low individualism. It was found to be insignificant in cultures characterized with low power distance and high individualism. The research contributes to the SEW research by exploring the relationship between context, SEW and performance thus helping in highlighting possible reasons of why previous research has been inconclusive regarding SEW and its relationship to performance. By comparing SEW across cultures, we notice the possibility that the underlying logic of the SEW approach might not be standard across contexts. To the researcher’s knowledge, contextualizing SEW research as a way to enhance knowledge of the field and addressing its criticism, has not been done despite its high relevance. Thus, the research addresses this perceived gap. In addition, the research makes a methodological contribution by confirming measurement invariance of the FIBER conceptualization of SEW, thus confirming its universal applicability. Finally, this research contributes to the understanding of the heterogeneity of family firms by highlighting the differences of SEW among them which is more evident across cultures. This study asserts the importance of contextualizing SEW research by demonstrating the variation in SEW and in its relationship with firm’s outcomes. It also provides samples of how to conduct context by sampling and context by comparing research and offers them a validated conceptualization of SEW construct. It opens new avenues of research by providing research agenda on how to further develop the research through context by theorizing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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 teacher head, 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".