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This study focuses on intra-industry determinants of acquisition performance. Seven years of printed research on acquisitions from 10 top-tier business journals is categorized on the basis of R&D intensity – that is, per industry classification: high-, medium-, and low-technology – and determinants of acquisition performance. Instead of broadly generalizing acquisition performance determinants across industries, this study focuses on how the practice of enhancing acquisition performance is different per industry classification and what acquiring firms need to take into account.

Product innovation is central to the success of most companies. The rewards of a successful innovation programme are highly visible in terms of sales, profits and growth. But not so apparent are the strategies that underlie these product innovation efforts. This monograph is about the ingredients of a winning new product strategy — about strategic decisions on markets, technologies, products — that result in a successful innovation programme.

Briefly reviews previous literature by the author before presenting an original 12 step system integration protocol designed to ensure the success of companies or countries in their efforts to develop and market new products. Looks at the issues from different strategic levels such as corporate, international, military and economic. Presents 31 case studies, including the success of Japan in microchips to the failure of Xerox to sell its invention of the Alto personal computer 3 years before Apple: from the success in DNA and Superconductor research to the success of Sunbeam in inventing and marketing food processors: and from the daring invention and production of atomic energy for survival to the successes of sewing machine inventor Howe in co‐operating on patents to compete in markets. Includes 306 questions and answers in order to qualify concepts introduced.

This paper compares four external technology acquisition channels' (foreign technology transfer, exporting, inter-industry R&D spillover and domestic technology transfer) contributions to Chinese high-tech enterprises' innovation in the moderating role of absorptive capacity (AC).

Using technological catch-up theory and China Statistics Yearbook on High Technology Industry, this research investigates 16 Chinese high-tech industries from 2004 to 2015 via negative binomial regression.

The results show that indigenous knowledge transfer improves foreign-funded enterprises' (FFEs) innovation, while transnational knowledge transfer is the main channel for state-owned enterprises' (SOEs) technology acquisition. AC strengthens the positive relationship between transnational knowledge transfer and innovation in SOEs and improves FFEs' innovation and exported tacit knowledge absorption.

This study contributes to the literature on external technology acquisition (ETA) and innovation by examining the different mechanism of ETA instead of isolated strategy considering the AC and ownership structure of Chinese high-tech enterprises, which enrich the technological catch-up theory.

In this paper, we examine the comparative advantage of Korea and China while focusing on their technology level. The three digit SITC (Standard International Trade Classification) data is classified by technology level and the revealed comparative advantage (RCA) is derived from 1992-2009 by using UN COMTRADE data. For careful interpretation of the comparative advantage and technology levels, we also examined intra-industry trade and unit values of bilateral Korea-China trade, and semi-conductor industry technology. We found that the revealed comparative advantage has moved from low technology products to high technology products in Korea. China still maintains a comparative advantage in low technology products such as textiles and clothing, but at the same time, China’s high and medium-high technology products have recently gained a comparative advantage. The perception that China only has a comparative advantage for labor intensive products with low technology should be changed based on our analysis. However, China’s advancement in technology should not be overestimated. When comparing the unit value of basic materials of Korea’s and China’s exports, we found that Korea’s export product prices are on average higher than that of China’s, although the gap is reducing. A wider technology gap between Korea and China still exists in the semi-conductor industry, which is one of the most advanced high technology industries throughout the world.

The librarian and researcher have to be able to uncover specific articles in their areas of interest. This Bibliography is designed to help. Volume IV, like Volume III, contains features to help the reader to retrieve relevant literature from MCB University Press' considerable output. Each entry within has been indexed according to author(s) and the Fifth Edition of the SCIMP/SCAMP Thesaurus. The latter thus provides a full subject index to facilitate rapid retrieval. Each article or book is assigned its own unique number and this is used in both the subject and author index. This Volume indexes 29 journals indicating the depth, coverage and expansion of MCB's portfolio.

This paper empirically explored the influence of external and internal factors on technological and non-technological innovation of 5747 Indian firms. The study also explored novel insights about manufacturing firms by segregating them into high-technology and low-technology industries.

The study employed hierarchical regression analysis to analyse a cross-sectional dataset gathered from the World Bank enterprise survey. The firms are segregated into high-technology and low-technology industries based on the technology-intensity classification of the manufacturing industry given by the Organisation for Economic Co-operation and Development.

The main results highlight that technological and non-technological innovation was primarily driven by internal resources and capabilities rather than external factors. The authors found the highest effect of research and development spending on both forms of innovation. In both high-tech and low-tech industries, technology transfer is positively associated with technological innovation and negatively associated with non-technological innovation. Furthermore, external business support has substantially influenced non-technological innovation in low-tech industries.

This study used two-step hierarchical regression to explore the influence of external and internal factors on technological and non-technological innovation separately. Exploring determinants of innovation in high-technology and low-technology industries also brings the distinct prerequisites of enhancing innovation to the attention of policymakers and industry experts.

For the studies whose purposes are to evaluate the relationship between industrial characteristics and innovation activities of the enterprises, there are some limitations in the measures of industrial characteristics and using traditional statistical techniques. The purpose of this paper is to investigate the relationship between industrial characteristics and innovation capabilities within Chinese high-tech industries using grey system theory. The research results show that grey system theory is suitable to investigate the relationship between industrial characteristics and innovation capabilities within Chinese high-tech industries.

This paper proposes the measures of industrial characteristics and innovation capabilities of high-tech enterprises. First, based on the data on Chinese large and medium-sized high-tech enterprises for the period of 2011-2013, this paper applies grey relational analysis to identify the relatively most important indexes on affecting innovation capabilities of Chinese high-tech enterprises. Second, based on the results from grey relational analysis, this study draws a ranking of the five Chinese high-tech industries in terms of innovation capabilities by grey decision making. Finally, based on the results from grey decision making, this study applies GM (0, N) model to investigate the relationship between industrial characteristics and innovation capabilities within Chinese high-tech industries.

The results of this study show that in the evaluation indexes system of innovation capabilities of high-tech enterprises, personnel in R & D institutions, R & D personnel, internal expenditure on R & D, expenditure on new product development, expenditure on technology imports, expenditure on technology renovation, and expenditure on technology assimilation and absorption are relatively most important elements affecting innovation capabilities of Chinese high-tech enterprises. In addition, the two top ranking on innovation capabilities are manufacture of electronic equipment and communication equipment, and manufacture of medicines. At last, the findings indicate that in the measures of industrial characteristics, the three top ranking on affecting innovation capabilities of Chinese high-tech enterprises are R & D intensity, technology absorption intensity of indigenous high-tech enterprises and foreign-invested enterprises size. The opening level is in the middle position. Technology intensity, market concentration, and state-owned enterprises size are the three bottom ranking on affecting innovation capabilities of Chinese high-tech enterprises.

This study has some limitations. First, this study is limited to Chinese high-tech industries. The findings may not be applicable to other countries’ high-tech industries. Further studies with other countries’ high-tech industries could be extended and examined how industrial characteristics affect innovation capabilities of the firms in these industries. Second, the measures of industrial characteristics proposed in this study are somewhat theoretically weak. In the future, the authors will further improve the current analysis, and develop the measures of industrial characteristics. Finally, with the advent of the more data with the consistent statistical coverage released by China’s National Bureau of Statistics during the more continuous years, other methods, such as panel data regression model in econometrics could be used to evaluate the relationship between industrial characteristics and innovation capabilities within Chinese high-tech industries. By then, the scholars can compare the results from grey system theory and those from panel data regression model in econometrics.

Appropriate industrial environment is favorable for Chinese high-tech enterprises to feed their innovation capabilities. Scientific evaluation on the relationship between industrial characteristics and innovation capabilities within Chinese high-tech industries is of great significance for Chinese high-tech enterprises in exerting technological catch-up and promoting their competitive advantage. The purposed measures of industrial characteristics and innovation capabilities of high-tech enterprises in this paper, and combined methodology based on grey system theory could be applied to evaluate the relationship between industrial characteristics and innovation capabilities of Chinese high-tech enterprises.

This paper proposes the measures of industrial characteristics and innovation capabilities of high-tech enterprises, and uses grey system theory to evaluate the relationship between industrial characteristics and innovation capabilities within Chinese high-tech industries.

The purpose of this paper is to examine the role of founding team experience (industry and venturing) in new venture creation. This paper posits the following questions: How does founding team experience influence the likelihood of new venture creation, in the nascent stage? How does industry context moderate this relationship? The study aims to fill an important gap in the literature by unpacking the impact of different types of founding team experiences on venture outcome, and by focusing on the influence of founding team in the venture creation process, specifically at the nascent stage.

The paper utilizes data from the Second Panel Study of Entrepreneurial Dynamics, a longitudinal data set of 1,214 nascent entrepreneurs in the USA. Logistics regression was employed to analyze the effect of founding team experience on new venture creation. Post hoc analysis was conducted to ensure the confidence of the findings.

The paper provides empirical insights about how founding team experience influences the likelihood of new venture creation in the nascent stage. At the nascent stage, founding team industry experience positively affects new venture creation while founding team venturing experience does not. However, in the high-technology industry environment, the influence of the founding team’s venturing experience on new venture creation is stronger than that in the low-technology industry environment.

Due to the design of the data set, there is a risk of “right-censoring” problem. Also, because the study used archival data on founding teams, the methodology did not allow for uncovering the underlying team processes and dynamics during the venture creation process based on learning from experience. Future studies are encouraged to examine other types of founding team experience and the underlying process-level factors on venture creation.

The paper provides important practical implications for nascent entrepreneurs/entrepreneurial teams on team assembling and composition. In general, a team with higher-level industry experience is critical for venturing success. A team with higher-level venturing experience is more desired in the high-technology industry.

This paper fulfills an important gap in the entrepreneurial team literature by highlighting the complex and nuanced ways in which founding team experience influences the likelihood of venture creation in the nascent stage of the firm, especially after incorporating the additional impact of the industry context.

This paper aims to examine the different impacts of six variables on firm technological innovation performance in different high-tech industries in China. Through a comparative analysis of data about growth enterprises market board (GEM)-listed companies, this study attempts to get some conclusions, to help firms in different high-tech industries use resources more rationally and to improve technological innovation performance more effectively.

This paper constructs semi-parametric models based on the relevant data of GEM-listed companies during 2010 to 2015 for different high-tech industries. These models can ensure that the influencing factors of firm technological innovation performance are no longer restricted to a particular aspect but can provide a comprehensive comparative analysis of the effects of factors on firm technological innovation performance in different high-tech industries.

The empirical results show that R&D expenditures have a significant positive impact on firm technological innovation performance in most high-tech industries, but not in electronic and communication equipment manufacturing industry; R&D personnel investment and government subsidies have significant positive impacts on firm technological innovation performance in knowledge-oriented industries; technology diversity has a significant positive impact on firm technological innovation performance in technology-oriented industries; the proportion of exports shows an inverted U-shaped relationship with firm technological innovation performance in electronic and communication equipment manufacturing industry, while firm size shows an inverted U-shaped relationship with firm technological innovation performance in general equipment manufacturing industry; and the effect of semi-parametric model fit is superior to the general parameters model.

Drawing on the resource dependence perspective, this paper is the first to consider a comprehensive treatment of differential effects of internal resources (R&D personnel, R&D expenditure), external resources (government subsides) and firm characteristics (firm size, export ratio) on firm technological innovation performance in different high-tech industries in an emerging country, in particular in contrast to previous studies that have focused on a single industry or taken the type of industry as a control variable. In addition, most studies about the determinants of firm innovation performance are based on survey questionnaires, which may introduce large subjective errors. Setting the relationship between variables in advance may also introduce fit error when using a general-parameter model. Semi-parametric regression which is used in this paper is able to prevent this shortcoming effectively. When constructing a regression model, this can be exempted from the formal constraints, thus estimating data more accurately and ensuring superior fit.