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It is suggested that, to be successful, innovation teams should be small and consist of people with key expertise who want to participate and develop new solutions within their organisations. When it comes to conducting innovation work, I suggest shared leadership may be a factor influencing success. In this chapter, a theoretical framework is presented on the shared leadership of innovation teams. The key to establishing shared leadership in innovation teams is to plan for it as the team is created, not after the team has already been formed, as this may result in various problems in the intended innovation project. The proposed framework details key aspects to consider; some of which are related to external factors such as management and resources, and some to internal factors such as the team’s size, competencies, and their ability to develop norms and ways of working together. The proposed framework is applicable for managers, innovation leaders, and team members, and contributes to previous research on shared leadership and innovation leadership. Further research on the proposed framework is suggested.

Big data have rapidly developed as a viable solution to many problems faced in engineering industries. Specifically, in the industry of water resource engineering, where there is a tremendous amount of data, various big data techniques could be applied to achieve innovative and efficient solutions for the industry. This study reviewed the proposal of big data as potential approaches to solve various difficulties encountered in managing water resources and related applications in Malaysia. The advantages and disadvantages of big data applications have also been discussed along with a brief literature review and some examples of case studies.

This chapter examines the acquisition of assets (real estate) and companies in the Chinese real estate industry. We propose a nuanced view of state ownership (beyond state being the largest shareholder) and argue that firms with a combination of state and private ownership may be in a unique position to acquire real assets. We conduct an analysis of the growth and funding of the industry for the period and also analyze the successful acquisitions in the industry over 2004–2007. Our analysis is supportive of the nuanced view about state-owned enterprises (SOEs) and their advantageous position for acquiring real estate assets from the government. Our analysis also sheds light on the two-stage marketization process in the Chinese real estate industry where SOEs endowed with real estate assets are sold to non-SOEs.

Purpose: In this chapter, the author evaluates the association between bank loan loss provisions (LLP) and the pre-provisions earnings of UK banks during the first-wave of the COVID-19 pandemic. A positive co-movement between the two variables indicates income smoothing.

Methodology: Graphical analysis, correlation analysis and regression analysis are used to assess the relationship between income smoothing and bank provisions among UK systemic banks.

Findings: The findings show that LLP have an inverted V-shaped property during the first-wave of COVID-19 pandemic. LLP reached its highest level at the peak of the pandemic in Q2 2020 and declined in the subsequent quarters. The regression results show that LLP are positively related to pre-provisions earnings during the pandemic quarters and in the pre-pandemic quarters. The relationship is stronger in the pandemic quarters and indicates higher income smoothing in the pandemic quarters. The correlation results also show a strong positive correlation between bank provisions and pre-provisions earnings in the pandemic period. In the individual bank analysis, three of the four systemic banks exhibit higher income smoothing during the pandemic quarters.

Implication: UK systemic banks engaged in earnings management as a coping mechanism to mitigate the effect of the pandemic on their profits.

Need for the study/originality: This chapter is the first to provide a preliminary analysis of income smoothing among banks during the early stages of the COVID-19 pandemic.

The new Silk Road provides cross-border-e-commerce firms with an opportunity to widen their markets. Under this circumstance, the preference recognition of countries and inventory allocation among overseas warehouses both become critical issues to solve. Three Chinese smartphone brands, including HTC, Huawei, and MI, are selected in this chapter for their relatively enormous sales. DHgate and AliExpress websites are chosen as platforms to analyze the sales for data availability. This chapter first depicts key features of the sales and then, based on which, divide countries into several groups according to their preference for phones by cluster analysis. Then, based on the results of cluster analysis, this chapter further models the inventory assignment among the seven major overseas warehouses that were built by AliExpress in 2015. The results show that the HTC seems to be pursuing the “high value with high price” strategy, while the other two companies seem to be pursuing a hybrid strategy of “low-price” strategy and “high value with low price” strategy. This chapter also provides an assignment pattern of inventory among the overseas warehouses based on the real data of sales and costs.