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The extant “supply‐side” frameworks of industry evolution fail to predict the evolutionary patterns in industries based on systemic technologies. This paper aims to describe the complex demand environment in industries based on systemic technologies and to explain how the continuously evolving demand structure influences the choice and level of firm investments in the above context.

The paper identifies a conceptual gap in the “technology‐centric” literature on industry evolution by conducting a detailed interpretive survey of the literature that focuses on the demand‐side determinants of firm‐ and industry‐level technological processes underlying industry evolution, and co‐evolution of the technological system underlying an industry and the consumer applications based on the same.

The paper provides a set of empirically verifiable mechanisms to explain competing firms' choice and level of investment under conditions of technological and demand uncertainty in industries based on systemic technologies. On one hand, firms' investments influence the evolution of both the technological system(s) and their constituent components that underlie such industries and, on the other, firms' investments influence the consumption of the array of consumer applications that are generated in these industries.

The theoretical explanation provided herein not only enhances the understanding of the role of demand‐side factors as determinants of rate and direction of technological advances but also lies central to the understanding of the evolution of industries based on systemic technologies. More specifically, the paper explains how the interaction between continuously evolving demand structure in the downstream market(s) for consumer applications and the technological components comprising the technological system influences competing firms' choice and level of investments.

Recently, some biologists have argued that the time has come to replace separation between Lamarckism and Darwinism with their connection. The aim of this paper is to understand whether this paradigm shift in the interpretation of biological evolution offers useful insights for dealing with the unresolved issue of how industries and their organizational populations evolve.

Lamarckism and Darwinism are two approaches that have contrasted or interwoven with each other in the study of biological evolution, just as they have in the study of organizational evolution. This paper provides a critical analysis of the long history of the debate through to the recent, revolutionary discoveries in evolutionary microbiology obtained in the wake of the genomic revolution.

From this new research frontier emerge three important findings: adaptive variations are no longer an anomaly that is peculiar to human organizations, but rather correspond to a widely observed phenomenon in the biological world; the same can be said for the process of horizontal replication; Lamarckism and Darwinism are not two mutually exclusive interpretations of evolution but two dimensions of evolution that coexist in various ways. Lamarckian dimension of evolution and the Darwinian one, handled in the light of these results, may help to understand the evolutionary logic that underpins specific stages of the history of industries.

The paper presents a new way of looking at industries and their firms from an evolutionary perspective.

Foundry, Design House, and integrated device manufacturers (IDM) are major characters in the semiconductor industry value chain. The purpose of this paper is to discuss patterns of characters' evolution in technology through patents classified as wafer‐design application patents and wafer‐process patents.

Various patent indicators, such as average patent citation count, and the combination of the average patent citation count and relative patent count share were used to measure the patent activity, patent quality, and the combination of the patent quality and relative patent activity share, respectively. The study period (1979‐2009) was divided into three major technology or wafer size eras, 1979‐1991 for the 6‐ and pre 6‐inch wafer era, 1989‐1999 for the 8‐inch wafer era, and 1997‐2009 for the 12‐inch wafer era.

Foundry has gradually become the technology transferor rather than purely the manufacturing capacity provider. Foundry's impact on the technology level has risen steeply on both the wafer‐process technology fields and the wafer‐design application technology fields. As a result, IDM, traditionally considered the primary technology contributor in the semiconductor value chain for the past 30 years, will continue to be challenged in the semiconductor industry.

Some hypotheses are clarified to provide managerial implications for the semiconductor industry. Owing to Foundry's rise in technology activity and quality, IDM/Design House should not merely view it as one of their capacity providers but should also pursue a technology alliance with it.

The paper clarifies the traditional hypotheses of the characters of technology in the semiconductor value chain.

This paper investigates whether style migration affects industry evolution. The study documents industry evolution in terms of market weights, returns, and risks over the sample period from 1966 to 2000. The study shows that investment styles migrate in different degrees across different industries over time. In addition, the relation between industry evolution and style migration is neither simple nor static. The paper shows that growth‐value migration has predictability about the industries' returns and changes in volatility. Furthermore, style migration in the industry is mainly driven by existing firms changing their investment styles, not by new entrants to the industry causing style shifts. Both investment theory and its application to investment management critically depend on our understanding of stock return persistence anomalies. The ability to outperform buy‐and‐hold strategies by acquiring past winning stocks and selling past losing stocks, commonly referred to as “individual stock momentum,” remains one of the most puzzling of these anomalies. Moskowitz and Grinblatt (1999) attribute the bulk of the observed momentum in individual stock returns to industry momentum—the tendency for stock return patterns at the industry level to persist. It is well known that there are hot and cold IPO markets, and hot and cold sectors of the economy. Investors may simply herd toward (away from) these hot (cold) industries and sectors, causing price pressure that could create return persistence. The recent attraction to internet stocks is perhaps the latest manifestation of such behavior, which is not unlike a similar pattern biotechnology firms and railroad firms witnessed in 1980s and 1900s, respectively. For the active portfolio manager, rotation among different industries may provide opportunities for portfolio performance enhancement. As a result, understanding both the evolution of industries and the style factors causing cyclical variation in industry returns and risk plays an important role in professional portfolio management. Given the fact that a number of researchers have found consistent differences among the returns of various equity classes, investment styles of size and growth‐value are natural candidates for studying what causes cyclical variation in industry returns and risks. Individual investment styles perform differently during various stages of a cycle of bull market and bear market. For example, small cap stocks outperformed large cap stocks in the 1970s, but large cap stocks outperformed small cap stocks in the 1980s. Growth stocks outperformed value stocks in 1998 while the opposite occurred in 1997. Although it is well documented that the cross‐sectional variation in expected returns can be captured by three factors: market, size, and book‐to‐market, it is not yet clear whether cyclical variations in style attributes, not style returns, influence cross‐sectional variation in expected returns and return variance. In the investment industry, cyclical variation in style attributes is commonly called style migration. Perez‐Quiros and Timmermann (2000) provide a rational suggestion that small firms are most strongly affected by tighter credit market conditions in a recession and thus cyclical variations in style performance result from business cycles. As certain equity classes took off and others fell out of favor, investors overreacted, thereby causing cyclical variations in returns and risks of industries where firms are similarly sensitive to the fundamental shocks. In a recent study of behavioral finance, Barberis and Shleifer (2003) argue that in the presence of switchers who can affect asset prices by moving funds across styles, a style‐level momentum strategy could be successful because good performance by a style attracts switcher flows, which then drive the prices even higher. Analyzing the extent of interaction between style migrations and industry evolution may shed light on understanding the sources of predictable components in industry returns and risk. This paper provides such a contribution to the literature. The rest of the paper is organized as follows. Section I describes the sample data and summarizes industry evolution in terms of market capitalization weights in the entire market over time. Section II analyzes style migration within each industry. Section III examines the effect of style migration on industry evolution. Section IV concludes.

This paper aims to examine whether in old industrial regions, the trajectory of clusters follows that of their corresponding industry or deviates from it and which are the factors that account for cluster evolution. This paper deals with the issue of how established clusters either renew or transform themselves in such regions and how they adapt to changes in their corresponding international industries.

This research paper draws from in-depth case studies on six industrial clusters, takes a longitudinal perspective and uses a multi-level and qualitative analysis. Based on existing literature, the paper suggests and exploratory analytical framework with four alternative scenarios for cluster evolution and three broad factors: cluster knowledge base, social capital at cluster and region-level and public policies.

Clusters do not always follow the life cycle of its dominant industry. The paper clearly shows a diversity of cluster evolution across clusters and even within clusters (at subcluster level). This study suggests that cluster knowledge diversity and heterogeneity allow to broaden the scope of evolutionary trajectories available; the same goes for social capital at cluster and region levels.

The main limitation of this paper lies in its qualitative approach that makes its conclusions more suggestive than conclusive. In any case, further research on other Basque clusters may corroborate or question its findings.

The paper offers an empirical and longitudinal study on cluster evolution, very much needed to the ongoing theoretical discussion on this issue. So far, there are very few empirical studies on cluster evolution with this perspective. At the same time, it presents a theoretical framework to analyse diversity of cluster evolution in old industrial regions that builds on Menzel and Fornah’s (2010) model.

The chapter presents a theoretical framework that deals with the basic question of how networks and industries coevolve. We draw upon the structural and relational perspectives of networks to theorize about changes occurring in interfirm networks over time and the coevolutionary linkage of these changes to the industry life cycle. We further extend the widely accepted industry life cycle model by claiming that industry-specific evolutionary patterns impact the structure of the network’s relations, which in turn lead to diversification in the sources of innovation and to variation in the patterns of industrial evolution.

There is a growing need for measures assessing technological changes in systemic contexts as business ecosystems replace standalone products. In these ecosystem contexts, organizations are required to manage their innovation processes in increasingly networked and complex environments. In this paper, we introduce the technology and ecosystem clockspeed measures that can be used to assess the temporal nature of technological changes in a business ecosystem. We analyze systemic changes in the personal computer (PC) ecosystem, explicitly focusing on subindustries central to the delivery of PC gaming value to the end user. Our results show that the time-based intensity of technological competition in intertwined subindustries of a business ecosystem may follow various trajectories during the evolution of the ecosystem. Hence, the technology and ecosystem clockspeed measures are able to pinpoint alternating dynamics in technological changes among the subindustries in the business ecosystem. We subsequently discuss organizational considerations and theoretical implications of the proposed measures.

The purpose of this paper is to elucidate the dynamic interactions and co‐evolution of institutions with the technology and organization fields in emerging industry development. Insights and inspirations from comparison of the triangle relationship among government, market and local community in different institutional contexts could contribute to possible institutional innovation in the context of large‐scale institutional transition. In this way, this paper is expected to offer insights to the development of emerging industries in China.

The paper reviews the focal literature focusing on institutional change and the co‐evolution of institution, industry and technology. A multi‐level conceptual framework is put forward to explain the mechanism for the co‐evolution of technology, organization and institution. A multi‐case comparison method was applied to compare and disclose the process of co‐evolution of institutions, and the technology and organizational fields, as well as varied paths of industry development in different institutional contexts.

Emerging industry development in China is still presenting the character of path dependence to a great extent under traditional institutional arrangement, while the power and possible contribution from broader actors in the local community have been ignored. Driving force for a more innovative institutional transition towards emerging industry development should consider decentralized institutional arrangement and actions at local community instead of “command and control” from central planning.

First, the comparison of wind energy industry development in three countries creates possibilities for further analysis and reference for China's emerging industry. Second, the illustration of the triangle relationship among government, market and local community in different countries helps policy makers in China reconsider and redesign an effective institutional framework for balancing the powers among indigenous community, local government and market. Institutional alignment should be listed as an important consideration during the process of the policy design of such an effective institutional framework.

The paper presents a model to understand the dynamic co‐evolution of the institution, technology and organizational fields. It confirms the role of institution in promoting emerging industry development. Particularly, it offers inspirations for the development of emerging industries in nations facing large‐scale institutional transition.

The thought and rationale of sustainable competitive advantage in strategy are significantly influenced by the Schumpeterian models of dynamic competition in IO and evolutionary economics. Yet, most analytical accounts of sustainable competitive advantage fail to explain how firms' investment choices influence, and are simultaneously influenced by, the co‐evolution of “external” industry competition and “internal” firm competences. This paper aims to contribute to the development of a theory of endogenous market structure in strategy.

Two alternative assumptions are developed – concerning temporally heterogeneous firm investment strategy – that lie central to a proposed behavioral theory of endogenous market structure. Additionally, a theoretical description is provided of the endogeneity of the demand‐side determinants of firm investment strategy and industrial market structure. Finally, guidelines are provided for empirical application of [incorporating] the alternative assumptions and theoretical arguments.

It is expected that the theoretical arguments in the paper will influence strategy scholars to develop dynamic models of firm performance that render themselves amenable to sound empirical analyses.

The paper contributes towards developing a theory of endogenous market structure in strategy.

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.