Social Capital and Firm Performance: The Impact of Technical Uncertainty

Yu, Sui-Hua; Chiu, W. Henry

doi:10.2139/ssrn.1658792

Cited by 7 publications

(13 citation statements)

References 42 publications

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“…Our study confirms earlier findings that investigate the effect of social networks on various measures of firm productivity (e.g., Stam et al, 2013). However, in contrast to Uzzi (1996) and Yu and Chiu (2013), our results generally indicate a linear relationship between density and productivity. In addition, our results indicate that, in some situations, observed productivity measures may be flawed if environmental factors such as transaction costs are ignored.…”

Section: Discussionsupporting

confidence: 91%

“…In particular, large trading networks and dense household networks promote farm productivity. In contrast to studies reporting a non-linear relationship between measures of closure and firm performance (Uzzi, 1996;Yu and Chiu, 2013), our results generally support only a linear relationship between density and farm productivity. Furthermore, our results strongly indicate that by ignoring transaction costs, both the absolute observed productivity measures as well as the productivity ranking change considerably.…”

Section: Introductioncontrasting

confidence: 99%

“…As transaction costs are usually latent variables, we have chosen social networks as a proxy for transaction costs; earlier studies (e.g., Di Matteo et al, 2005;Henning et al, 2012) have shown that social networks can reduce transaction costs by positively affecting information acquisition or trade activities. Because the empirical relationship between structural parameters of social networks and productivity is possibly non-linear (Stam et al, 2013;Yu and Chiu, 2013), we have chosen to apply and to compare different parametric, semiparametric, and nonparametric specifications of the regression function to measure the effect of ego-centered farm networks on farm productivity.…”

Section: Discussionmentioning

confidence: 99%

“…As earlier empirical studies find different functional relationships between social network parameters and measures of firm performance (Yu and Chiu, 2013;Stam et al, 2013) we apply and compare different parametric, semiparametric, and nonparametric specifications of the regression function in our analysis. The results show strong consistency over most estimation models and support our hypothesis and earlier findings that social networks have a significantly positive effect on farm productivity.…”

Section: Introductionmentioning

confidence: 99%

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Transaction costs and social networks in productivity measurement

Henningsen

Henning

2014

Empir Econ

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We argue that in the presence of transaction costs, observed productivity measures may in many cases understate the true productivity, as production data seldom distinguish between resources entering the production process and resources of a similar type that are sacrificed for transaction costs. Hence, both the absolute productivity measures and, more importantly, the productivity ranking will be distorted. A major driver of transaction costs is poor access to information and contract enforcement assistance. Social networks often catalyse information exchange as well as generate trust and support. Hence, we use measures of a firm's access to social networks as a proxy for the transaction costs the firm faces. We develop a microeconomic production model that takes into account transaction costs and networks. Using a data set of 384 Polish farms, we empirically estimate this model and compare different parametric, semiparametric, and nonparametric model specifications. Our results generally support our hypothesis. Especially large trading networks and dense household networks have a positive influence on a farm's productivity. Furthermore, our results indicate that transaction costs have a measurable impact on the productivity ranking of the farms.

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Section: Discussionsupporting

confidence: 91%

Section: Introductioncontrasting

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Transaction costs and social networks in productivity measurement

Henningsen

Henning

2014

Empir Econ

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“…; Villena et al . ; Yu and Chiu ) have identified inverted‐U‐shaped relationships between SC and performance; that is, SC is beneficial initially, but after exceeding some threshold level, it acts as a constraining and restricting force. The remaining three studies (Batjargal ; Rouzies and Hulland ; Stam and Elfring ) have observed significant moderating/interactive effects of particular aspects of SC on firm‐level performance.…”

Section: Reviewing the Negative Effectsmentioning

confidence: 99%

The Negative Effects of Social Capital in Organizations: A Review and Extension

Pillai

Hodgkinson

Kalyanaram

et al. 2015

Int J Management Reviews

132

100

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Numerous studies have examined the positive effects of social capital in organizations, whereas the possible negative effects have attracted considerably less scholarly attention. To rectify this imbalance, this paper first undertakes a rigorous review of the published scholarly empirical evidence pertaining to the negative effects of social capital in organizations through a search of Web of Knowledge and Scopus, and then enumerates six potentially negative effects arising from increased levels of social capital. The review focuses on negative effects arising from bonding social capital and those arising from dense networks and closure, advancing new theory to elucidate the generative mechanisms that give rise to the proposed negative effects. Finally, the authors identify potential moderators of the negative effects thus theorized. Using the lens of social identification theory, the authors argue that dysfunctional identification processes restrict the processing of information and stimulate over-commitment to established relationships, diluting in turn the dialectical process, and inhibiting individual learning within organizations, culminating in groupthink, the postponement of structural adjustments, the non-rational escalation of commitment, and the blurring of firms' boundaries. This review thus furthers the agenda of a more balanced inquiry into the effects of social capital in organizations.

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Market ranking and network structure: pathway to dominance

Sarabi

Smith

McGregor

et al. 2021

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PurposeThe relationship between interlocking directorates and firm performance has been increasingly debated, with a focus on whether firm's centrality in interlock networks is associated with performance. The purpose of this study is to examine not only how a firm's position in this network is associated with performance but also how the performance of network partners can impact a firm's performance. This study examines how firms effectively utilise the interlock network to achieve the goal of higher market capitalisation – termed market capitalisation rank (MCR).Design/methodology/approachThe premise of the study is the UK FTSE 350 firms from 2014 to 2018. The paper makes use of a temporal network autocorrelation model to examine how firm characteristics, the structural position in the interlock network and the performance of network partners affect MCR over time.FindingsThe analysis indicates that firms with ties (via the interlock network) to firms with high market capitalisation are more likely to enhance their own MCR, highlighting network partners have the opportunity to play a critical role in a firm's dominance strategy to optimise firm value.Originality/valueThe value of this research is that it does not only look at the impact of a firm's position in the network on performance, but the impact of the performance of network partners on a firm's market performance as well.

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Social Capital and Firm Performance: The Impact of Technical Uncertainty

Cited by 7 publications

References 42 publications

Transaction costs and social networks in productivity measurement

Transaction costs and social networks in productivity measurement

The Negative Effects of Social Capital in Organizations: A Review and Extension

Market ranking and network structure: pathway to dominance

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