Strength of Strong Ties: Empirical Evidence from the Construction Waste Hauling Business in Hong Kong
Publication: Journal of Management in Engineering
Volume 40, Issue 6
Abstract
The so-called strength of weak ties (SWT) theory asserts that weak relationships are more instrumental than strong ones in job searching and sharing. Yet, some posit the opposite: job opportunities tend to be shared through strong ties in some business areas. Nevertheless, little empirical research has been conducted to substantiate the hypotheses and unravel the rationales behind them. This paper attempts to contribute empirical evidence to this management field by focusing on the construction waste hauling business in Hong Kong. Four null hypotheses about the relationships between haulers’ tie strength and job opportunity sharing (defined as waste hauling order sharing) are proposed, and then a big data set containing 11.7 million waste hauling records is analyzed to test the hypotheses. The analysis shows that, in general, the stronger the tie strength of two haulers, the higher the quantity of job opportunities they share. Among all ties a hauler owns, the greater the proportion of strong ties, the higher the quantity and quality (e.g., shorter distance and less underloading) of job opportunities will be shared. However, these positive effects will be diminished when the strength or proportion exceeds a certain point. These empirical findings not only highlight the strength of strong ties, providing an empirical supplement for the long-standing SWT theory, but also exemplify an exploration of applying SWT theory in the construction management field. Moreover, this study provides practical implications for construction waste hauling businesses to improve efficiency and the public sector to pursue social optimality in construction waste management.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request, specifically including (1) a small sample of the entire big data set; and (2) the metric computation code.
Acknowledgments
This research was supported by the Hong Kong Research Grants Council (RGC) Humanities and Social Sciences Prestigious Fellowship Scheme (HSSPFS) 2022/2023 (Project 37000622).
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Received: Dec 22, 2023
Accepted: May 23, 2024
Published online: Jul 30, 2024
Published in print: Nov 1, 2024
Discussion open until: Dec 30, 2024
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