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Owing to dual constraints including liability of foreignness and liability of origin when emerging multinationals internationalize, they inevitably face the challenge of overseas legitimation. However, few studies have explored how latecomers cross the threshold of legitimacy in the dynamic context of transnational operation. The purpose of this paper is to unravel the evolution process, triggers and specific strategies of overseas legitimacy threshold crossing of emerging multinationals.

Through the longitudinal case study of Haier Group and Goldwind Sci & Tech Co., Ltd, this study investigates the periodical characteristics of overseas legitimacy threshold crossings and the co-evolution among critical factors influencing the legitimation process in the host country.

First, it summarizes that the legitimacy threshold in the host country experiences a sequential process from pragmatic legitimacy to normative legitimacy, and finally cognitive legitimacy. It is an inevitable choice for emerging multinational enterprises to realize and sustain legitimation from passive adaptation to active creation. Second, it reveals that the triggers for crossing the threshold of overseas legitimacy include periodically dynamic factors – international network linkage and resource system reconfiguration, as well as cross-stage spiral interaction effects. Third, it determines the specific strategies for crossing the threshold of overseas legitimacy, namely, replacement, upgrading and reconstruction of organizational identity, and reveals the important role of insisting on the country-of-origin Facebook in promoting the legitimation.

This study enriches the legitimacy threshold crossing literature from an evolutional perspective, especially the traditional static legitimacy research. This study also reveals the key impacting factors – international network linkage and resource system reconfiguration – and their evolution process interacted with the legitimation process.

The emerging multinationals should break the stereotypes from developed markets in that only creating new cognitive patterns through active legitimate strategies can they truly cross the legitimacy threshold in the host country. The emerging multinationals also need to retain their own home country legitimacy traits – Facebook and balance the relation between the image of the home country and the image of host country.

This paper investigates the process of overseas legitimacy threshold crossing for emerging multinationals in a dynamic context of transnational operation, particularly with respect to the evolutionary role played by international network linkage and resource system reconfiguration.

Effective policy to support business ecosystems should build on evidence-based analyses of firm-level activities and outcomes. This paper aims to contribute to this requirement and makes three contributions. The first contribution is to extend the application of the network capital concept to a variety of eight distinct linkage categories (e.g. suppliers, customers and business support agencies) that support networking and clustering, in both activity and impact terms. The second contribution is outlining a novel method of network visualisation (V-LINC) based on the collection of primary and qualitative data. The third contribution is in applying the method to one cluster, information and communications technologies.

Qualitative research on the nature and extent of organisational network linkages was undertaken. Structured interviews with a set of focal firms followed a tailored design approach. The concept of network capital was extended and applied to the cluster context by measuring network inputs and output (i.e. investments and impact). The approach was operationalised via a novel impact measurement approach, denoted as V-LINC, an acronym for visualising linkages in networks and clusters.

The authors develop a business impact framework exploiting novel linkage visualisations and qualitative data from firms in a cluster in one city region across eight linkage types to capture distinct network capital elements. Organisational inputs into network development, measured as investment and involvement indicators and organisational outcomes from those networks, measured as importance and intensity indicators, are used to assess network performance. A comprehensive, systematic and robust analysis of network elements and performance is possible. Distance is found to interact differently across linkage types. Targeted recommendations may be made from the analysis of local or regional business ecosystems in light of measured business impacts of linkages.

Due to the resource-intensive nature of data collection, the current study engages a limited sample of firms and interviewees. Applications of this approach in other contexts will permit further research into its usefulness in evaluating business impacts generated through networking activities.

The method introduced here (V-LINC) offers a novel means to include both geography network theory into an understanding of knowledge relationships and networks within clusters. Accounting for both distance and linkage type reveals which categories of intra-regional and extra-regional linkages generate the greatest impact, given their frequency. The approach adds to available cluster visualisation and analysis approaches through identifying patterns of disaggregated knowledge flows and their impacts, with application to evaluation demands of policy.

This chapter estimates heterogeneous productivity growth and spatial spillovers through industrial linkages in the United States and China from 1981 to 2010. The authors employ a spatial Durbin stochastic frontier model and estimates with a spatial weight matrix based on inter-country input–output linkages to describe the spatial interdependencies in technology. The authors estimate productivity growth and spillovers at the industry level using the World KLEMS database. The spillovers of factor inputs and productivity growth are decomposed into domestic and international effects. Most of the spillover effects are found to be significant and the spillovers of productivity growth offered and received provide detailed information reflecting interdependence of the industries in the global value chain (GVC). The authors use this model to evaluate the impact of a US–Sino decoupling of trade links based on simulations of four scenarios of the reductions in bilateral intermediate trade. Their estimation results and their simulations are as mentioned based on date that ends in 2010, as this is the only KLEMS data available for these countries at this level of industrial disaggregation. As the GVC linkages between the United States and China have expanded since the end of their sample period their results can be viewed as informative in their own right for this period as well as possible lower bounds on the extent of the spillovers generated by an expanding GVC.

This study examines the dynamic linkages between the Indian Nifty index futures traded on the offshore Singapore Exchange (SGX) and US stock indices (DJIA, NASDAQ and S&P 500) under the closure of the spot market for Nifty futures.

With high-frequency 5-min overlapping price data, the authors employ the Johansen cointegration test to investigate long-run relationships, the Granger causality test to assess short-run dynamics and the BEKK-GARCH model for volatility spillover investigation.

The empirical findings reveal that the SGX Nifty futures market is cointegrated with the US DJIA market. The US DJIA stock index strongly influences the price discovery of SGX Nifty futures and past innovations in the US markets strongly impact the current volatility of SGX Nifty futures.

Findings from this study have significant implications for market participants, particularly foreign investors and portfolio managers. These findings might be helpful for market participants to improve the prediction power of expected SGX Nifty futures price and volatility, especially under the closure of the spot market. Also, SGX market participants can take the significant role of the US market into account when formulating hedging and trading strategies with Indian Nifty futures. Besides, our findings have significant implications for policymakers in evaluating market stability.

This article adds to the very limited research on offshore or international stock index futures; it is the first study that empirically examines the international linkages of offshore SGX Nifty futures under the closure of its underlying spot market and also the driving force behind the linkages.

The purpose of this paper is to contextually theorise the different patterns of emerging multinational companies’ (EMNCs’) learning processes for innovation and the different influences of their technology-driven FDIs (TFDIs) on the processes.

A comparative case study method and process tracing technique are employed to investigate how and why firms’ learning processes for innovation took place, how and why the TFDIs emerged and influenced the firms’ learning processes in different ways.

The paper identifies two different patterns of learning process for innovation (Glider model vs Helicopter model) and two different roles of the case firms’ TFDIs (accelerator vs starter) in the different contexts of their learning processes. It is found that the capability building of the domestic wind energy industry has an important influence on the case of EMNCs’ learning processes and thus on the roles of their TFDIs.

The limitation of the paper lies in its small number of cases in a specific industry of a specific country. The two contextually identified learning models and roles of TFDIs may not be applied to other industries or other countries. Future research should investigate more cases in broader sectoral and geographic scope to test the models and also to identify new models.

For EMNCs, who wants to use the Helicopter model to rapidly gain production and innovation capability, cross-cultural management and integration management are crucial to practitioners. For emerging countries with ambitions to explore the global knowledge and technology pool, besides of the EMNC’s capability building, the capability building in the domestic industries should not be overlooked by policy makers.

The paper develops a dynamic and contextual analytical framework which helps to answer the important questions about how and under what context a TFDI emerges and influences firm’s learning process for innovation. It theorises the EMNCs’ learning process and TFDIs in the context of the development of the domestic industry. It strengthens the explanatory power of the learning-based view and adds new knowledge to the current FSA/CSA discourse in the international business literature.

The authors aim to report empirical linkages between the US and Brazil, Russia, India and China (BRIC) financial stress indices catalyzing catalyzing dependent economic policy initiatives (an extended version of Singh and Singh, 2017a).

Initially, the study develops financial stress indices for the respective BRIC financial markets. Later, it captures linkages among the said US-BRIC indices by using Johansen cointegration, vector autoregression/vector error correction models (VECM), generalized impulse response functions, Toda–Yamamoto Granger causality, variance decomposition analyses and bivariate generalized autoregressive conditional heteroskedasticity (GARCH) model under constant conditional correlation framework, in general. Markov regime switching and efficient causality tests proposed by Hill (2007) are also used.

Overall, there are both short-run and long-run dynamic interactions observed between the US and Indian financial stress indices. For rest of the markets, only short-run interactions are found to be in existence. The time-varying co-movement coefficients report financial contagion impact of the US financial crisis on Russian and Indian financial systems only. Contrary to this, Brazilian and Chinese financial systems are largely exhibiting interdependence with the US financial system. Efficient causality tests report indirect impact of the Russian financial system on Brazilian via auxiliary Indian financial system.

The present study is the first of its kind capturing linkages among the US-BRIC financial stress indices by using diverse econometric models. The results support different market participants and policymakers in understanding effectiveness and implementation of economic policies while considering their cross-market interactions as well.

Global value chains (GVC) incorporate internationally fragmented sources of knowledge so as to increase global competitiveness and performance. This paper sheds light on the role of Industry 4.0 technological capabilities in facilitating knowledge access from international linkages and improving firm productivity.

Drawing on organizational learning research, the present study argues that the relationship between GVC breadth, analyzed in respect to the geographical fragmentation of production facilities and productivity follows an inverted U-shaped pattern that can be explained by the interplay between external knowledge access and the coordination costs associated with GVC breadth. We test our predictions using a purpose-built survey that was carried out among a sample of 426 Spanish manufacturing firms.

Our results indicate that organizations adhering to a traditional manufacturing system are able to benefit from fewer transnational relationships (concretely 11 foreign facilities) in the search for productivity improvements. This can be largely attributed to the marginal value of the knowledge accessed and the costs of coordinating international counterparts' production and knowledge transfer. However, our study reveals that the adoption of Industry 4.0 technologies has the potential to broaden optimal GVC breadth, in terms of the number of linkages to interrelate with (concretely 131 foreign facilities) so as to obtain productivity gains while mitigating the complexities associated with coordination.

The study unveils that Industry 4.0 technologies enable management of broader GVC breadth, facilitating knowledge access and counteracting coordination costs from international counterparts.

With the globalization and liberalization in terms of increasing financial flows across the countries, the policy makers around the world are not independent in the context of monetary and fiscal policy initiatives. In this regard, this paper aims to attempt to quantify and capture long run, short run as well as time-varying linkages among the two financial stress indices, namely, Kansas City Financial Stress Index (KCFSI) and Indian Financial Stress Index (IFSI) across the monthly period (2004 to 2014).

Owing to the non-existence of a standardized financial stress index with regards to the Indian financial system, the study has developed an index/stress indicator using principal component analysis. Furthermore, to comprehend the linkages, the study uses bivariate Johansen cointegration model, vector error correction model, impulse response functions (IRF), variance decomposition analysis (VDA), Toda-Yamamoto’s Granger causality test and, finally, bivariate generalized autoregressive conditional heteroskedastic (BVGARCH) (1,1) model under constant conditional correlation (CCC) framework.

The results report a stochastic trend among the two indices wherein the US financial system acts as a source of a shock causing disequilibrium in the long run co-movement. About 40 per cent of the adjustments take place in one month and rest in the coming months. Both the IRF and VDA report a greater degree impact of the US financial stress on the Indian financial system. Moreover, there is a uni-directional short run causality running from the stress in the US financial system to the Indian financial stress. Furthermore, the co-movement between the US and Indian financial stress reached to its maximum significant level during the sub-prime crisis even confirmed by the Markov switching model results.

Overall, the results provide an insight to the financial market investors both domestic as well as international in their act of risk management. The financial stress prevailing in an economy further has an impact on different economic factors like foreign exchange rates, interest rates, yield curves, equity market returns and volatility. So, the empirical results support strong implications for the Indian policy makers as well as investors in the Indian financial markets.

The present study contributes to the literature in three senses. First, the study considers indices reflecting financial stress in the Indian as well as US financial system. Second, the study captures long run as well as short run linkages among the financial stress indices relating to a developed and an emerging market. Finally, the study uses CCC-BVGARCH (1,1) model to account for the time-varying co-movement among the financial stress indices. This helps in comprehending time-varying nature of the co-movement of the stress in the financial system prevalent in the respective markets.

The studies on international housing markets have not modeled frequency domain and focused only on the time domain. The purpose of the present research is to fill this gap by using the state-of-the-art econometric technique of wavelets to understand how differences in the horizon of analysis across time impact international housing markets’ relationship with some of the key macroeconomic variables. The purpose is to also analyze the direction of causation in the relationships.

The author uses the novel time–frequency analysis of international housing markets’ linkages to the macroeconomic drivers. Unlike conventional approaches that do not distinguish between time and frequency domain, the author uses wavelets to study house prices’ relationship with its drivers in the time–frequency space. The novelty of the approach also allows gaining insights into the debates that deal with the direction of causation between house price changes and macroeconomic variables.

Results show that the relationship between house prices and key macroeconomic indicators varies significantly across countries, time, frequencies and the direction of causation. House prices are most related to interest rates at the higher frequencies (short-run) and per capita income growth at the lower frequencies (long-run). The role of industrial production and income growth has switched over time at lower frequencies, particularly, in Finland, France, Sweden and Japan. The stock market’s nexus with the housing market is significant mainly at high to medium frequencies around the recent financial crisis.

The present research implies that in contrast to the existing approaches that are limited to the only time domain, the frequency considerations are equally, if not more, important.

Results show that interested researchers and analysts of international housing markets need to account for the both horizon and time under consideration. Because the factors that drive high-frequency movements in housing market are very different from low-frequency movements. Furthermore, these roles vary over time.

The insights from the present study suggest policymakers interested in bringing social change in the housing markets need to account for the time–frequency dynamics found in this study.

The paper is novel on at least two dimensions. First, to the best of the author’s knowledge, this study is the first to propose the use of a time–frequency approach in modeling international housing market dynamics. Second, unlike present studies, it is the first to uncover the direction of causation between house prices and economic variables for each frequency at every point of time.