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We examine the evolution of vertical specialization in three industries: chemicals, computers, and semiconductors. Vertical specialization is the restructuring of industry-wide value chains, such that different stages are controlled by different firms, rather than being vertically integrated within the boundaries of individual firms. In some cases, vertical specialization may span international boundaries and is associated with complex international production networks. After decades of vertical specialization, firms in the chemical industry are re-integrating stages of the value chain. By contrast, the semiconductor and computer industries have experienced significant vertical specialization during the past ten years. We examine how and why these contrasting trends in vertical specialization have co-evolved with industry maturation and decline, and underscore the importance and role of both industry factors and business strategies necessary for industries to become more specialized. We also consider the effects of vertical specialization on the sources of innovation and the geographic redistribution of production and other activities. We conclude that the evolution of vertical specialization in these three industries has both reflected and influenced the strategies of leading firms, while also displays industry-specific characteristics that are rooted in different technological and market characteristics.

The extant literature has established that industry-specialist auditors gain performance-enhancing industry-specific sub-specialty knowledge (e.g., Solomon, Shields, & Whittington, 1999) via training and on the job experience. This knowledge has been shown to allow specialists to outperform non-specialists on a range of industry-specific tasks. The current study extends this line of research by comparing and contrasting the relative performance gains enjoyed by industry-specialist auditors in two different industry settings, one regulated and the other unregulated. When specializing in regulated industries, auditors gain very detailed industry-specific knowledge which is not the case for specialists in unregulated industries (Dunn & Mayhew, 2004). By comparing industry-specialists to non-specialists with matching industry-based experience, this study measures the relative benefits of specialization in different industry settings, rather than the benefits of specialization per se, which has been well established in the literature. This study finds that the performance gains made by regulated industry-specialists significantly outweigh those made by unregulated industry-specialists on industry-specific tasks. The implications of these results for future research and practice are explored in the body of the chapter.

“It should also be noted that the objective of convergence and equal distribution, including across under-performing areas, can hinder efforts to generate growth. Contrariwise, the objective of competitiveness can exacerbate regional and social inequalities, by targeting efforts on zones of excellence where projects achieve greater returns (dynamic major cities, higher levels of general education, the most advanced projects, infrastructures with the heaviest traffic, and so on). If cohesion policy and the Lisbon Strategy come into conflict, it must be borne in mind that the former, for the moment, is founded on a rather more solid legal foundation than the latter” European Commission (2005, p. 9)Adaptation of Cohesion Policy to the Enlarged Europe and the Lisbon and Gothenburg Objectives.

Traditional industry was initially built with kinship, cultural value, and unique characters representing a particular system of production. However, current industry challenges pressurized traditional industry bond of primordial system with the need of adaptations to survive. Some traditional industry may resist the twenty-first-century challenges and pressures, but many of them are transforming their cultural and production characters to adapt modern business competitions. Indonesian traditional furniture industry Jepara has their familial system of productions which constitute “flexible specialization” where particular kinship and work contract created from a very specialized household small-scale furniture producer. However, this production system in fact struggles and is contrasted with the community needs to survive in the industry. The likely occurring progress of traditional industry are then remaining on the senior members of the industry to preserve knowledge which has empowered over many generations, while the younger generations consider transforming their ability for survivability and better financial rewards.

This chapter is the further elaboration of how Indonesian rural traditional furniture industry in Jepara presents its survivability and whether it is sustainable. This chapter exemplifies participants’ quotes and statements which create anxiety toward their future, cultural value, bond of industry kinship, and doubting their ability to withhold global and local pressures.

We are interested in modeling the impact of spatial and interindustry dependence on firm-level innovation of Chinese firms The existence of network ties between cities imply that changes taking place in one city could influence innovation by firms in nearby cities (local spatial spillovers), or set in motion a series of spatial diffusion and feedback impacts across multiple cities (global spatial spillovers). We use the term local spatial spillovers to reflect a scenario where only immediately neighboring cities are impacted, whereas the term global spatial spillovers represent a situation where impacts fall on neighboring cities, as well as higher order neighbors (neighbors to the neighboring cities, neighbors to the neighbors of the neighbors, and so on). Global spatial spillovers also involve feedback impacts from neighboring cities, and imply the existence of a wider diffusion of impacts over space (higher order neighbors).

Similarly, the existence of national interindustry input-output ties implies that changes occurring in one industry could influence innovation by firms operating in directly related industries (local interindustry spillovers), or set in motion a series of in interindustry diffusion and feedback impacts across multiple industries (global interindustry spillovers).

Typical linear models of firm-level innovation based on knowledge production functions would rely on city- and industry-specific fixed effects to allow for differences in the level of innovation by firms located in different cities and operating in different industries. This approach however ignores the fact that, spatial dependence between cities and interindustry dependence arising from input-output relationships, may imply interaction, not simply heterogeneity across cities and industries.

We construct a Bayesian hierarchical model that allows for both city- and industry-level interaction (global spillovers) and subsumes other innovation scenarios such as: (1) heterogeneity that implies level differences (fixed effects) and (2) contextual effects that imply local spillovers as special cases.