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Describes an empirical study which aimed to compare the marketing practices used by high‐tech and low‐tech companies in the UK, and to attempt to explain any significant differences. Concludes that there is increasing awareness of the salience of marketing by UK high technology companies, though they are still not as market oriented as low‐tech ones. Suggests that broadly speaking results replicate earlier findings in US high technology firms, with the critical difference that the British companies rate both the possession of the latest technology and price competition less seriously than the American organizations.

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.

Though certain characteristics of micro-firms affect the likelihood of their participation in external relationships, how cooperation in craft enterprises differs from low and high-tech enterprises has not been investigated yet. Therefore, this study aims to fill the above gap in the literature.

This study adopts a descriptive approach by extensively reviewing relevant literature to explore the unique characteristics and nature of micro-firm's co-operative behavior. The theoretical approach of this research is grounded in resource-based view and dynamic capabilities theories.

This study finds that handicraft micro-firms possess special and unique characteristics that differentiate them from low- and high-tech firms. Further, handicraft micro-firms' co-operative behavior also differs from other firms in terms of cooperation motives, breadth, depth and factors that inhibit or promote cooperation. Additionally, in small handicraft firms, the co-operation is more informal, personal and through social networks, whereas in the corporate sector, it is more formal, direct and through supply chains. This study also argues that contrary to handicraft and low-tech firms, high-tech firms are more likely to cooperate with external partners and invest heavily in R&D for new product development (often radical in nature).

This study enriches our understanding of handicraft micro-firms' special and unique characteristics that differentiate them from low- and high-tech micro-firms. This research also provides in-depth knowledge to understand the handicraft micro-firms’ co-operative behavior and how it differs from low- and high-tech firms.

This paper aims to identify the influence of innovation network and technological learning on innovation performance of high-tech cluster enterprises.

Using a questionnaire, data are collected from Dalian High-tech Industrial park in China. In addition, structural equation model is used to identify the influence of innovation network and technological learning on the innovation performance of high-tech cluster enterprise.

The findings of this study show that the centrality of network location and the strength of the network relationship have a direct positive effect on technology acquisition, technology digestion and technology exploit of high-tech cluster enterprises. Meanwhile, technology acquisition has a direct positive effect on technology digestion, technology digestion has a direct positive effect technology exploit, and technology exploit has a direct positive effect innovation performance of high-tech cluster enterprises.

To improve innovation performance, high-tech cluster enterprises should not only nurture and optimize innovation networks but also improve technological learning ability.

This paper empirically supports the significant influence of innovation network and technological learning on innovation performance. While the results provide guidance for researchers and practitioners, it also adds value to innovation-related research.

The authors seek an answer to the research question: how do the disclosure of the intended use of initial public offering (IPO) proceeds and firm characteristics jointly influence IPO performance?

Data on the use of proceeds, firm age, size, high- or low-tech industry, and the length of the use of proceeds section were collected from 341 IPOs in the USA, UK, and Hong Kong. Fuzzy-set Qualitative Comparative Analysis was used to predict which configurations of IPO use of proceeds and firm characteristics consistently led to above-average IPO performance.

Ten configurations of causal factors were found to lead to above-average IPO performance. Disclosure of IPO proceeds use matters for IPO performance but is contingent on firm characteristics. Whether a firm is in a high- or low-technology industry along with its size and age have distinct effects on which intended uses of proceeds are beneficial and how long their intended proceeds section must be to lead to above-average IPO performance.

These findings contribute to a multidimensional view of IPO performance. The authors use information processing and a management perspective to see how the use of proceeds sections help frame an IPO’s equity story. The use of a configurational methodology and a management perspective shows how IPOs can be viewed as a bundle of attributes.

The purpose of this paper is twofold: first, to analyse the impact of intellectual capital (IC) and growth opportunities on firms’ financial performance as well as the moderating effect of IC on the relationship between growth opportunities and financial performance; and second, to analyse the impact of IC on growth opportunities.

The current study uses a sample of non-financial listed firms consisting of 14 Western European countries for the period between 2004 and 2015. The estimation method used is specifically the Generalised Method of Moments system (1998) estimator, a dynamic panel estimator.

The results reveal that the IC efficiency of the current period has a positive impact on the financial performance of high-, medium- and low-tech European firms. A non-linear relationship was found between growth opportunities and financial performance. Also, findings suggest that the positive relationship between growth opportunities and financial performance is enhanced with the efficient use of firms’ IC. Results indicate that the efficient use of IC in the current period has a greater impact on growth opportunities in high firms. Additionally, results reveal the presence of a non-linear relationship between ownership concentration and growth opportunities.

The current study contributes to the current literature by exploring a sample of firms across Western European countries, which is divided among high-, medium- and low-tech firms. The econometric modelling enables the author to conduct a longitudinal study.

The purpose of this paper is to determine if the relationship between growth and research and development (R&D) intensity is of a different nature in the context of low‐ and high‐tech Portuguese service small to medium‐sized enterprises (SMEs).

The System Analysis of Iberian Balance Sheets database is used. Based on the European Union's recommendation, L124/36 (2003/261/CE), the authors select 764 low‐tech and 139 high‐tech Portuguese service SMEs for the period 1999‐2006. As method of analysis, panel data are used.

A negative relationship between growth and R&D intensity for low‐tech Portuguese service SMEs is identified, whatever the level of R&D intensity. For high‐tech Portuguese service SMEs, a quadratic U‐shaped relationship between growth and R&D intensity is identified. Moreover, the authors find that relationships between growth and determinants are of a special nature in the context of high‐tech Portuguese service SMEs with high levels of R&D intensity.

It is recommended that as far as possible the managers/owners of low‐tech Portuguese service SMEs, and especially high‐tech ones with non‐high levels of R&D intensity, hire qualified human resources and make more continuous investment in R&D. The authors advise managers/owners of high‐tech Portuguese service SMEs with high levels of R&D intensity to establish stable relationships with creditors. Policy‐makers should increase financial support directed, above all, to innovative Portuguese service SMEs.

The paper is pioneering in presenting different relationships between growth and R&D intensity in the context of low‐ and high‐tech service SMEs.

The purpose of this paper is to explain how internal and external sources of knowledge influence the innovation performance (IP) in Italian manufacturing firms and how different these relationships are for low-technology (LT) and high-technology (HT) firms.

The study proposed a model relating external knowledge, internal knowledge and IP that was tested using Bayesian structural equation modeling with a sample of Italian manufacturing firms of Community Innovation Survey 2010. It was run separately for high-tech firms (including HT and medium-HT aggregations of manufacturing industries of NACE Rev. 2) and low-tech firms (including LT and medium-LT aggregations).

The results showed a difference between high-tech and low-tech manufacturing firms in Italy. The investments to leverage internal knowledge sources are important for high-techs and not significant for low-techs. On the other hand, the level of external KS improves significantly the IP of low-techs and has a negative effect for high-techs. The level of absorptive capacity is central to improve the positive effect of the external knowledge on the IP for all firms, but it is still underdeveloped.

The effects of 2008 economic crisis hit the Italian manufacturing industry specifically hard and are still felt. Innovation is a solution for firms’ growth and Italy is considered a below-average innovator country in Europe. The study could identify important gaps in Italian manufacturing firms that hinder their innovative performance improvement.

The purpose of this paper is to explore and compare the extent of intellectual capital (IC) and its four components in high-tech and non-high-tech small and medium-sized enterprises (SMEs) operating in China’s manufacturing sector, and to examine the relationship between IC and the performance of high-tech and non-high-tech SMEs.

The study uses the data of 116 high-tech SMEs and 380 non-high-tech SMEs listed on the Shenzhen stock exchanges during 2012–2016. The modified value added intellectual coefficient (MVAIC) model is used incorporating four components, namely, capital employed, human capital, structural capital and relational capital. Finally, multiple regression analysis is utilized to test the proposed research hypotheses.

The findings of this paper reveal that there is significant difference in MVAIC between high-tech and non-high-tech SMEs. The results further indicate a positive relationship between IC and financial performance of high-tech and non-high-tech SMEs. Specifically, IC is positively associated with firms’ earnings, profitability and operating efficiency. Additionally, capital employed efficiency, human capital efficiency and structural capital efficiency are found to be the most influential value drivers for the performance of two types of SMEs while relational capital efficiency possesses less importance.

This paper will provide a valuable framework for executives, managers and policy makers in managing IC within the Chinese context.

To the best knowledge of the authors, this is the first empirical study that has been conducted on high-tech and non-high-tech SMEs in the manufacturing sector in China.

The extant literature on initial public offerings (IPOs) generally assumes that a high degree of pre‐IPO leverage serves as a positive signal of firm quality as it forces a firm's managers to adhere to tough budget constraints. The purpose of this paper is to question the validity of this assumption when it is indiscriminately applied to all firms, while other potentially important determinants of a firm's optimal capital structure are ignored. High‐tech versus low‐tech firms are specifically focused on.

Multivariate regression controlling is used for various firm and offer characteristics, market and industry returns, and potential endogeneity between investment bank rankings, price revisions, and under‐pricing.

It is found that debt only serves as a signal of better firm quality for low‐tech IPOs, as reflected in smaller price revisions and lower under‐pricing. For high‐tech IPOs, the effect of leverage is reversed: for these firms, higher leverage is associated with increased risk and uncertainty as reflected by higher price revisions and greater under‐pricing. The results remain significant after controlling for various firm variables as mentioned above.

The research results allow managers of high‐tech firms that contemplate going public to better understand the effect their company's capital structure will have on the pricing of their IPO. Prior research generally suggests that – irrespective of a firm's underlying characteristics – higher financial leverage results in lower under‐pricing. The findings highlight the falsity of this generalization and point out that it only holds for low‐tech firms. Firms that operate in a high‐tech sector, on the other hand, will leave less money on the table if they use equity rather than debt financing.

It is shown that leverage only serves as a positive signal for low‐tech firms. The IPOs of these firms generally undergo smaller price revisions and are less under‐priced than the IPOs of low‐tech firms that use little debt in their capital structure. While this result is consistent with earlier studies, it is show that the relationship between these variables reverses for high‐tech IPOs. Specifically, it is found that high‐tech IPOs with high leverage undergo larger price revisions and are more under‐priced than high‐tech firms with low leverage. In contrast to earlier findings, this suggests that for high‐tech IPOs, higher leverage implies increased ex‐ante uncertainty and risks.