Structure and complexity in six supply chains of the Brazilian wind turbine industry

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Vivian Sebben Adami ◽  
Jorge Renato Verschoore ◽  
Miguel Afonso Sellitto
Keyword(s):  
Social Network ◽  
Supply Chains ◽  
Transaction Costs ◽  
Wind Turbine ◽  
Manufacturing Industry ◽  
Structural Complexity ◽  
Network Size ◽  
Due Dates ◽  
Content Type ◽  
Network Metrics

PurposeThe purpose of this article is to compare design choices and assess the structural complexity of six manufacturing supply chains (SCs) of the Brazilian wind turbine industry.Design/methodology/approachThe research method is quantitative modeling. This study adopts the social network perspective to provide a broad set of network metrics for comparative analysis and characterization of the structural configuration and complexity of SCs. Transaction costs and the risk of disruption supported the metrics employed in the study. Network size, network density, core-size and centralization metrics stem from transaction costs, whereas constraint and betweenness centrality stem from risk of disruption.FindingsThe main conclusion is that, in the Brazilian wind manufacturing industry, increasing the SC structural complexity by adding redundant ties to minimize disruption risks, even implying higher transaction costs, increases the capacity to win orders.Research limitations/implicationsOnly the Brazilian wind turbine industry was studied. Therefore, findings are not general, but specific, to the case.Practical implicationsManagers and practitioners of the Brazilian wind turbine industry should focus on increasing the complexity of their SCs, even if it increases transaction costs, to ensure due dates compliance in orders.Originality/valueTo the best of the available knowledge, there is no commonly accepted or shared measurement for SC complexity, and this study proposed an alternative approach to bridge this research gap, the structural perspective of social networks. Traditional measures were complemented by new metrics, and the power of the application of social network analysis to SC investigations was empirically demonstrated in different levels of analysis.

Information privacy disclosure on social network sites

2016 ◽  
Vol 7 (3) ◽  
pp. 282-300 ◽  
Author(s):  
Kai Li ◽  
Xiaowen Wang ◽  
Kunrong Li ◽  
Jianguo Che
Keyword(s):  
Social Network ◽  
Social Network Sites ◽  
Academic Research ◽  
Information Privacy ◽  
Network Size ◽  
Perceived Benefits ◽  
Content Type ◽  
Perceived Risks ◽  
Personal Innovativeness ◽  
Research Fields

Purpose As social network sites (SNS) have increasingly become one of the most important channels for communication, the related privacy issues gain more and more attention in both industry and academic research fields. This study aims to connect the antecedents of information privacy disclosure on SNS. Design/methodology/approach Based on exchange theory, this study tries to investigate the decision-making process for information privacy disclosure on SNS. Factors from both user’s and website’s perspectives are taken into account in the proposed model. Findings The results suggest that an individual’s perceived benefits will increase their willingness to disclose information privacy on SNS, but perceived risks decrease this kind of willingness. The authors also find social network size, personal innovativeness and incentive provision positively affect people’s perceived benefits. Originality/value Moreover, privacy invasion experience enhances perceived personal risks, but website reputation helps to reduce perceived risks.

scholarly journals Impact of supply chain on the competitiveness of the automotive industry

2019 ◽  
Vol 54 (2) ◽  
pp. 205-225
Author(s):  
Carlos Sakuramoto ◽  
Luiz Carlos Di Serio ◽  
Alexandre de Vicente Bittar
Keyword(s):  
Supply Chain ◽  
Supply Chains ◽  
Transaction Costs ◽  
Automotive Industry ◽  
Chain Structure ◽  
Strategic Decision ◽  
Chain Model ◽  
Horizontal Structure ◽  
Content Type ◽  
Structural Problems

Purpose There is a great reliance on fiscal incentives to sustain the automotive industry competitiveness due to several structural problems, among them the inefficiency of the supply chain. This paper aims to compare the supply chain structure of traditional automotive industry with the supply chains from South Korea and China. Based on strategic decision and transaction cost theory, this comparison seeks to exploit the factors that led to the inefficiency of automotive supply chains. Design/methodology/approach The authors used a qualitative approach and applied a multi-method research. They conducted semi-structured interviews with six executives from automakers representing the selected countries, carried individual meetings during one workshop and used secondary data from several sources. Findings Concepts identified in the research such as reliability, supply chain governance and automaker competencies led the authors to propose that the traditional automakers have higher transaction costs when compared to the new automakers due to the horizontal structure of their supply chain. While new competitors have vertical upstream supply chains, which indicates better profitability, traditional automotive industry is horizontal, depends on fewer Tier 1 suppliers and is disconnected from Tier 2, impacting negatively in the transaction costs and supply chain management. Practical implications This study suggests that automotive executives rethink the current upstream supply chain model by identifying the competencies required for their current and future competitiveness and implementing a vertical integration of these competencies. Originality/value This research exploited the inefficiency of supply chain as one of the explanations for the low competitiveness of the national automotive industry.

Examining factors that influence information disclosure on social network sites from the perspective of network externalities

2019 ◽  
Vol 119 (4) ◽  
pp. 774-791 ◽  
Author(s):  
Samar Mouakket ◽  
Yuan Sun
Keyword(s):  
Social Network ◽  
Information Systems ◽  
Information Disclosure ◽  
Network Externalities ◽  
Personal Information ◽  
Social Network Sites ◽  
Network Size ◽  
Subjective Norms ◽  
Content Type ◽  
Perceived External Prestige

Purpose The purpose of this paper is to develop a research framework by drawing on the network externalities research and previous literature on information systems to understand the antecedents of information disclosure. The framework postulates that the following network externalities are important factors affecting social network sites (SNS) perceived usefulness (PU): perceived external prestige, referent network size and perceived complementarity. In addition, the paper proposes that PU, habit and subjective norms significantly affect information disclosure of SNS among Chinese users. Design/methodology/approach Data are collected from 251 Chinese university students who use SNS. Structural equation modeling was applied to test the hypotheses presented in the model. Findings The findings provide support for all the hypotheses, with the exception of the influence of referent network size on PU and the influence of subjective norms on information disclosure. Both perceived external prestige and perceived complementarity have reported positive effect on PU of SNS. In turn, the authors have found that PU and habit have positive effects on information disclosure. Originality/value SNS encourage users to reveal personal information by allowing them to post photos and videos and share their interests and feelings on the site. Yet, limited empirical research has investigated the concept of self-disclosure of personal information particularly among Chinese users of SNS. To fill this research gap, the authors have developed a research framework by drawing on the network externalities research and previous literature on information systems to understand the antecedents of information disclosure.

The effect of network externality on mobile social network site continuance

2015 ◽  
Vol 49 (3) ◽  
pp. 289-304 ◽  
Author(s):  
Tao Zhou
Keyword(s):  
Social Network ◽  
Structural Equation ◽  
Network Size ◽  
Perceived Usefulness ◽  
Network Externality ◽  
Equation Modeling ◽  
Privacy Concern ◽  
Mobile Social Network ◽  
Content Type ◽  
User Adoption

Purpose – The purpose of this paper is to examine the effect of network externality on users’ continuance of mobile social network sites (SNS). Design/methodology/approach – Based on the 230 valid responses collected from a survey, structural equation modeling was employed to examine the research model. Findings – The results indicated that network externality, which includes referent network size and perceived complementarity, has a significant effect on perceived usefulness and flow. Privacy concern affects perceived usefulness, flow and privacy risk. These three factors determine continued use. Originality/value – Previous research has focussed on the effects of motivations such as perceived value on user adoption of SNS. The effect of network externality on user continuance has seldom been examined. This research tries to fill the gap.

The inverted U-shaped relationship between the network profile and the competitive advantage of supply chains

2018 ◽  
Vol 29 (4) ◽  
pp. 1379-1400 ◽  
Author(s):  
Artur Swierczek
Keyword(s):  
Supply Chains ◽  
Competitive Advantage ◽  
Network Size ◽  
Curvilinear Relationship ◽  
Content Type ◽  
Network Profile ◽  
Network Properties ◽  
Multiple Network ◽  
The Relationship ◽  
The Ideal

PurposeThe purpose of this paper is twofold. First, the author aims to explore if there is a curvilinear relationship between the network rent, generated as a combination of the relational performance of two dyads and the network profile of the triadic supply chains. Second, the author seek to recognize the ideal network profile, consisting of the properties at the node and relationship level, that provides the highest network rents, and thus enables to increase the competitive advantage of supply chains.Design/methodology/approachThe paper opted for an exploratory study using a survey of triads forming supply chains. In order to reveal the capability of yielding the network rent in the examined triads, multiple regression analysis with interaction effects was employed. Having confirmed the existence of supernormal profit, the author investigated the relationship between the network rent and the network index. Finally, a cluster analysis was conducted to compare the network profile in the group of triads generating higher network rents with the cluster yielding relatively lower network rents.FindingsThe obtained findings show that a combination of the relational performance of two dyads contributes to generating the network rent, and thus ensures a more favorable competitive position of supply chains. The results of the study also indicate that there is a significant curvilinear, inverted U-shaped relationship between the network profile and the competitive advantage of triadic supply chains. In addition, the following network properties appear to be particularly important for yielding higher network rents: network centrality, betweenness, network density and network size.Originality/valueThe study contributes to the theory by testing if the network rent can be yielded as a combination of the relational performance of two dyads in the triadic supply chains. The research also indicates that there is a curvilinear relationship between the network rent and the network profile of examined supply chains. Moreover, the study also addresses the link between the network profile, consisting of the multiple network properties simultaneously, in relation to the competitive advantage of supply chains.

The assurance of food safety in supply chains via relational networking

2015 ◽  
Vol 35 (12) ◽  
pp. 1662-1687 ◽  
Author(s):  
Tobias Schoenherr ◽  
Ram Narasimhan ◽  
Piyas (P) Bandyopadhyay
Keyword(s):  
Supply Chain ◽  
Social Network ◽  
Food Safety ◽  
Risk Aversion ◽  
Supply Chains ◽  
Food Product ◽  
Systematic Investigation ◽  
Learning Orientation ◽  
Worst Case ◽  
Content Type

Purpose – Taking a social network perspective, the purpose of this paper is to develop a framework for the assurance of food safety via relational networking. Design/methodology/approach – The authors consider both informal and formal relational networking, and explore a firm’s learning orientation, risk aversion and consumer pressure as potential precursors to such relational networking. It is further hypothesized that relational networking generates both industry and supply chain knowledge, which is suggested to be beneficial for contamination detection. The model is tested with survey data collected among food-producing firms in India, the world’s second largest food producer. Findings – The authors find a positive influence of consumer pressure on both a firm’s learning orientation and risk aversion, which in turn affect both informal and formal relational networking. Informal networking further generated industry knowledge and was beneficial for contamination detection. Formal relational networking influenced supply chain knowledge, which in turn enabled contamination detection. Originality/value – Recent food product-related safety breaches, which have, in the worst case, led to fatalities, illustrate the importance of food safety in supply chains. This study represents the first systematic investigation of relational networking in the context of food safety from the perspective of social network theory.

Aligning supply chain transportation strategy with industry characteristics

2015 ◽  
Vol 45 (9/10) ◽  
pp. 837-860 ◽  
Author(s):  
Jian-yu Fisher Ke ◽  
Robert J. Windle ◽  
Chaodong Han ◽  
Rodrigo Britto
Keyword(s):  
Supply Chain ◽  
Supply Chains ◽  
Manufacturing Industry ◽  
Transportation Cost ◽  
Global Supply Chains ◽  
Data Set ◽  
Content Type ◽  
New Findings ◽  
Air Freight ◽  
The Impact

Purpose – The purpose of this paper is to propose that transportation modal mix in global supply chains is a result of the strategic alignment between industry characteristics and supply chain strategies. Design/methodology/approach – Using annual US trade statistics and manufacturing industry data for the years 2002-2009 between the USA and its top 12 Asian trading partners, this study applies various regression methods to examine key factors associated with the transport modal decision. Findings – The results show that industry characteristics have an impact on the transportation modal mix in global supply chains. Manufacturing industries use more air freight and less ocean freight when facing positive sales surprises, high-monthly demand variation, a high-contribution margin ratio, a high cost of capital, and increased competition. Practical implications – The findings provide important insights for logistics managers and freight forwarders. While transportation cost remains an important concern, a logistics manager must also consider non-cost factors such as competition, working capital, and demand uncertainties in their modal decisions. Freight forwarders should be supply chain solution providers who consider all of these industry factors and suggest a proper mix of transportation modes for their customers. Originality/value – This study is among the first efforts to examine the impact of industry characteristics on the transportation modal mix in global supply chains. This study first develops a theoretical framework for the modal choice decision for international transportation movements and then, using an extensive and innovative data set, provides new findings regarding current air freight practices in global supply chains.

A classification scheme for representing the variation in business and supply chain performance in Indian manufacturing industry

2017 ◽  
Vol 24 (4) ◽  
pp. 1013-1036 ◽  
Author(s):  
Gunjan Soni ◽  
Rambabu Kodali
Keyword(s):  
Supply Chain ◽  
Supply Chains ◽  
Manufacturing Industry ◽  
Clustering Algorithm ◽  
Classification Scheme ◽  
Research Work ◽  
Large Data ◽  
Supply Chain Performance ◽  
Manufacturing Companies ◽  
Content Type

Purpose The purpose of this paper is to identify a classification scheme which represents the variation in business and supply chain performance of supply chains in Indian manufacturing industry. Classification is done by presenting an empirical taxonomy of clusters representing supply chains in Indian manufacturing industry based on variation in supply chain excellence index (SCEI) and business performance index (BPI). Design/methodology/approach The clustering of supply chains in Indian manufacturing industry is done by considering BPI and SCEI as clustering variables, which were found by using survey responses and results of a prior empirical study which was carried out in Indian manufacturing industry. The cluster analysis is performed by using Ward’s agglomerative hierarchical clustering followed by using K-means clustering algorithm to establish final set of clusters. Findings It was found that supply chains in Indian manufacturing industries can be clustered in four major clusters which are named as strategic, celebrity, capable and undeveloped cluster. The characteristics of these clusters reveal some major characteristics of supply chains in Indian manufacturing industry. Originality/value The research work presented in this paper takes a novel way to introduce the clusters of supply chains in Indian manufacturing industry. The researchers who are seeking patterns in large data sets of manufacturing companies of Indian industry will be benefitted by using the proposed clusters. While practitioners who are seeking to move their supply chain one step ahead will also reap the benefits of the paper by seeking the characteristics of particular cluster.

Extraverts Have Larger Social Network Layers

2011 ◽  
Vol 32 (3) ◽  
pp. 161-169 ◽  
Author(s):  
Thomas V. Pollet ◽  
Sam G. B. Roberts ◽  
Robin I. M. Dunbar
Keyword(s):  
Social Network ◽  
Social Relationships ◽  
Outer Layer ◽  
Network Size ◽  
Emotional Intensity ◽  
Emotional Closeness ◽  
Network Layer ◽  
Network Layers ◽  
Social Network Size

Previous studies showed that extraversion influences social network size. However, it is unclear how extraversion affects the size of different layers of the network, and how extraversion relates to the emotional intensity of social relationships. We examined the relationships between extraversion, network size, and emotional closeness for 117 individuals. The results demonstrated that extraverts had larger networks at every layer (support clique, sympathy group, outer layer). The results were robust and were not attributable to potential confounds such as sex, though they were modest in size (raw correlations between extraversion and size of network layer, .20 < r < .23). However, extraverts were not emotionally closer to individuals in their network, even after controlling for network size. These results highlight the importance of considering not just social network size in relation to personality, but also the quality of relationships with network members.

scholarly journals Relationships between medical students’ co-regulatory network characteristics and self-regulated learning: a social network study

2021 ◽  
Author(s):  
Derk Bransen ◽  
Marjan J. B. Govaerts ◽  
Dominique M. A. Sluijsmans ◽  
Jeroen Donkers ◽  
Piet G. C. Van den Bossche ◽  
...  
Keyword(s):  
Social Network ◽  
Medical Students ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Learning Opportunities ◽  
Network Size ◽  
Tie Strength ◽  
Network Characteristics ◽  
Self Regulated Learning ◽  
Regulated Learning

Abstract Introduction Recent conceptualizations of self-regulated learning acknowledge the importance of co-regulation, i.e., students’ interactions with others in their networks to support self-regulation. Using a social network approach, the aim of this study is to explore relationships between characteristics of medical students’ co-regulatory networks, perceived learning opportunities, and self-regulated learning. Methods The authors surveyed 403 undergraduate medical students during their clinical clerkships (response rate 65.5%). Using multiple regression analysis, structural equation modelling techniques, and analysis of variance, the authors explored relationships between co-regulatory network characteristics (network size, network diversity, and interaction frequency), students’ perceptions of learning opportunities in the workplace setting, and self-reported self-regulated learning. Results Across all clerkships, data showed positive relationships between tie strength and self-regulated learning (β = 0.095, p < 0.05) and between network size and tie strength (β = 0.530, p < 0.001), and a negative relationship between network diversity and tie strength (β = −0.474, p < 0.001). Students’ perceptions of learning opportunities showed positive relationships with both self-regulated learning (β = 0.295, p < 0.001) and co-regulatory network size (β = 0.134, p < 0.01). Characteristics of clerkship contexts influenced both co-regulatory network characteristics (size and tie strength) and relationships between network characteristics, self-regulated learning, and students’ perceptions of learning opportunities. Discussion The present study reinforces the importance of co-regulatory networks for medical students’ self-regulated learning during clinical clerkships. Findings imply that supporting development of strong networks aimed at frequent co-regulatory interactions may enhance medical students’ self-regulated learning in challenging clinical learning environments. Social network approaches offer promising ways of further understanding and conceptualising self- and co-regulated learning in clinical workplaces.

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