Purpose: – This paper aims to identify the antecedents of firm’s supply chain agility (SC agility) and how SC agility impacts on firm’s performance. Design/methodology/approach: – Based on a comprehensive literature review, a conceptual model was proposed, in which the interrelated hypotheses were tested by structural equation modelling methodology using a dataset collected from 266 Chinese electronics firms. Findings: – Initially, it was found that SC integration and external learning positively influenced SC agility. Second, the results indicated that firm’s performance is positively impacted by SC agility. Moreover, SC agility also fully mediated the effect of SC integration on firm’s performance and the effect of external learning on firm’s performance. Research limitations/implications: – The generalizability of this research sample might be the major limitation of this study. Therefore, future research can adopt other industry sectors samples, such as automobile manufacturing, or other country samples to validate the research model. Practical implications: – This research outlines strategies for better preparedness to achieve SCs to be agile which is a core competency of electronic firms in emerging market. Findings reveal that the external coordination practices – external learning and SC integration – are important factors of SC agility. In addition, the findings contribute to understanding the important role of SC agility in improving firm’s performance. Originality/value: – This research examines the impact of two antecedents (i.e. SC integration and external learning) on SC agility and is the first empirical research to analyze the mediation effect of SC agility on the relationship between SC integration and firm performance and the relationship between external learning and firm performance.

This study explores how big data can be used to enable customers to express unrecognised needs. By acquiring this information, managers can gain opportunities to develop customer-centred products. Big data can be defined as multimedia-rich and interactive low-cost information resulting from mass communication. It offers customers a better understanding of new products and provides new, simplified modes of large-scale interaction between customers and firms. Although previous studies have pointed out that firms can better understand customers’ preferences and needs by leveraging different types of available data, the situation is evolving, with increasing application of big data analytics for product development, operations and supply chain management. In order to utilise the customer information available from big data to a larger extent, managers need to identify how to establish a customer-involving environment that encourages customers to share their ideas with managers, contribute their know-how, fiddle around with new products, and express their actual preferences. We investigate a new product development project at an electronics company, STE, and describe how big data is used to connect to, interact with and involve customers in new product development in practice. Our findings reveal that big data can offer customer involvement so as to provide valuable input for developing new products. In this paper, we introduce a customer involvement approach as a new means of coming up with customer-centred new product development.

A series of product harm scandals, ranging from toxic toys to peanut butter indicates that firms and consumers alike are vulnerable to quality risks in a global supply chain. The matter is exacerbated with a low ‘visibility’ of quality risks hidden in the multi-tier global supply networks. The threat of quality risks could be from raw materials, manufacturing processes, or logistics operations in any tier of the supply network. This research argues that better visibility of risk in supply tiers could minimise the quality threat. A product quality risk and visibility assessment framework, integrating both the incremental calculus and marginal analysis, is proposed. Case study results indicate that the proposed approach has the following benefits: (a) enables firms to have a better ‘visibility’ of quality risks in a multi-tier supply network; (b) allows firms to establish risk indices for product components; and (c) a traceable justification path for supplier selection.

Although various studies have emphasized linkages between firm competencies, networks and sustainability at organizational level, the links between top management tangible competencies (TMTCs) (e.g. contemporary relevant quantitative‐focused education such as big data analytics and data‐driven applications linked with the internet of things, relevant experience and analytical business applications), relationship‐based business networks (RBNs) and environmental sustainability have not been well established at micro‐level, and there is a literature gap in terms of investigating these relationships. This study examines these links based on the unique data collected from 175 top management representatives (chief executive officers and managing directors) working in food import and export firms headquartered in the UK and New Zealand. Our results from structural equation modelling indicate that TMTCs are the key determinants for building RBNs, mediating the correlation between TMTCs and environmental sustainability. Directly, the competencies also play a vital role towards environmental practices. The findings further depict that relationship‐oriented firms perform better compared to those which focus less on such networks. Consequently, our findings provide a deeper understanding of the micro‐foundations of environmental sustainability based on TMTCs rooted in the resource‐based view and RBNs entrenched in social network theory. We discuss the theoretical and practical implications of our findings, and we provide suggestions for future research.