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Much of the scholarly works on open innovation have significantly highlighted the application of the model in high-tech industries in the developed world. However, how the phenomenon applies in low-tech small and medium-sized enterprises (SMEs) in developing countries is still marginal and lacks substantive research. This study aims to draw on the network theory of innovation to examine the open innovation orientations of low-tech SMEs in an emerging market context, particularly Ghana.

The research design used was a qualitative–quantitative approach: the qualitative phase of the study, involving 31 low-tech SMEs, used a multiple case approach through semi-structured interviews and analyzed the interview responses using NVivo statistical tool; the quantitative phase, including 706 low-tech SMEs, also used a survey questionnaire approach and descriptively analyzed data collected using SPSS statistical tool.

Results disclose that the low-tech SMEs’ employment of the open innovation model are preponderantly driven by commercialization purposes, knowledge acquisition motives, financial motives and strategic motives, whereas their open innovation approaches include inbound strategies (collaboration with suppliers, co-creation/customer immersion), outbound strategies (IP licensing out) and coupled strategies (strategic alliances, contract manufacturing, and joint ventures). Moreover, the findings show that the SMEs’ preferred open innovation partners include suppliers, customers, private universities and non-industry, in that order. Finally, results show that the low-tech SMEs’ open innovation advantages include market gains, strategic gains, knowledge gains, operational gains, financial gains and network gains, whereas their open innovation challenges colossally were collaboration barriers and organizational barriers.

These findings purvey valuable perceptiveness for managers, academicians and policymakers alike; they highlight the importance of open innovation to low-tech SMEs, proven strategies, challenges involved and the mechanisms for effective and efficient adoption of the open innovation model.

The value of this study reclines in the extension of open innovation research from high-tech industries in the advanced world to low-tech SMEs in emerging economies. Results of the study enrich the knowledge and understanding of how the theoretical model of open innovation is adopted and implemented by the low-tech SME sector in emerging economies.

The aim of this paper was to attempt to investigate the transformation of traditional Chinese medicine (TCM) industry in Guangdong Province of China by applying a perspective of sectoral system of innovation (SSI). TCM industry in China has experienced an evolution path from low-tech to modern industry.

An analytical framework of sectoral system innovation for explaining the change in TCM industry in Guangdong Province has been conducted.

It shows that during the successful transformation of the TCM industry in Guangdong from low-tech to modern sector, national and provincial institution are acting as main drivers. Knowledge integration is the decision factor of modernization and innovation strategy as an actor that makes the transformation adjust and operate efficiently. Other actors, such as demand and external networks interplay together and led to a gradual organizational, structural and institutional change and modernization of TCM industry.

SSI analyses of TCM in China have never been conducted before, this paper also contributes to enrich the experience of low-tech industry transformation and provide references to other low-tech industries around the world.

Through the resource-based view (RBV) and contingency theory, this study empirically investigates the impacts of smart manufacturing systems' maturity levels on the performance of small and medium-sized enterprises (SMEs). Moreover, it aims to examine how industry types (i.e. high- and low-tech industries) and human-resource factors (i.e. the proportion of production workers to total workers) as contingency factors influence the effects of smart manufacturing systems.

The study conducted an empirical investigation of a sample of 163 Korean manufacturing SMEs. This study used an ordinary least squares regression to examine the impacts of the maturity levels of smart manufacturing systems on financial performance. Moreover, the impacts on operational efficiency were analysed using data envelopment analysis based on bootstrap methods and Tobit regression.

The RBV results indicate that the higher the maturity levels of smart manufacturing systems, the higher the financial performance and operational efficiency. Moreover, based on contingency theory, this study reveals that the effect of the maturity levels of smart manufacturing systems on financial performance and operational efficiency depends on firms' industry types and the proportion of production workers.

This study shows that the introduction of smart manufacturing systems can help SMEs achieve better financial performance and operational efficiency. However, their effectiveness is contingent on firms' industry types and the characteristics of their human resources.

Since the effects of the maturity levels of smart manufacturing systems on SME performance differ depending on their industries and the characteristics of human resources, managers need to consider them when introducing or investing in smart manufacturing systems.

Based on the RBV and contingency theory, this is the first empirical study to examine the moderating effects of industry types and the proportion of production workers on the impacts of the maturity levels of smart manufacturing systems on the financial performance and operational efficiency of SMEs.

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.

This study aims to explore the strategic impact of R&D and export activity on the diverse dimensions of US manufacturing firms’ performance. It also explores, using a predictive analytic model, the interactive synergistic effect that R&D and exports have on firm performance.

This study presents an innovative two-stage regression-neural network approach. Complementing conventional statistical analysis, the predictive backpropagation neural network explores the relative impact of R&D and exports and their synergistic effect on firm performance.

This study demonstrates the significant and positive effect of R&D and export strategy/activity on the economic performance of leading US manufacturing firms, particularly on their market-based performance (i.e. sustained growth rate or SGR). Furthermore, this study finds that the synergistic effect of R&D and exports on short-term performance (i.e. return on investment) is positive in high-tech firms but negative in low-tech firms. However, the synergistic effect on SGR is increasingly positive regardless of the level of technology.

In addition to traditional statistical analysis, this study uniquely investigates the relative importance of selected strategic variables, along with R&D and export activity and their differential synergistic effects, for firms’ economic performance in contrasting industry settings (high-tech vs low-tech).

Corporate reputation is regarded as an intangible asset which differentiates a firm from others and attracts customers to repurchase and willingly pay a premium price for products. However, despite the perceptive association between reputation and financial performance, empirical studies report inconclusive results. The purpose of this study is to investigate this link more comprehensively using four different reputation attributes and firm characteristics in the context of high‐ vs low‐tech companies.

This study operationalizes the corporate reputation as the four measures of Fortune's “America's Most Admired Companies” of 2008 and matched the companies with financial performance and firm characteristics measures from COMPUSTAT Research Insight for the period between 2001 and 2005. A total of 230 firms (108 in high‐tech vs 122 in low‐tech) over the same period were selected and stepwise multiple regression analysis probed the relationship between the corporate reputation and performance.

The key finding of this study is that such variables as corporate reputation are significantly and positively related with most indices of corporate performance measures while debt leverage affects profitability negatively. It was surprising to find that innovativeness turned out to have no impact on financial performance in both high‐ and low‐tech firms. The positive association between social responsibility and firm performance appeared to be partially supported because it showed significant impact on market‐based performance, but not on accounting‐based performance.

This study confirms the resource‐based view that a valuable, inimitable, and non‐substitutable asset such as corporate reputation leads firms to enhance financial and market performance. However, the effect is contingent on firm characteristics such as firm size, R&D intensity, debt leverage ratio, and capital intensity. Corporate reputation appears to emerge as a critical dimension of benchmarking of a firm performance.

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.

Efficiency and productivity has always being a key issue in economic science. The analysis of the impact of research and development (R&D) has been extensively studied in industries and countries of more or less aggregated level. This chapter aims to investigate the impact of corporate R&D in performance of low-tech industries, medium-tech, and high-tech in OECD countries.

This chapter aims to answer the questions: Is the impact of R&D significant for all types of industries? If so, what are the differences and the magnitude of these effects in each of these types of industries?

To this end, an unbalanced data set from 2000 to 2011 was collected for the main countries of Europe and the United States concerning low-, medium-, and high-tech to analyze the impact of the magnitude of corporate R&D and capital accumulation on productivity of these industries. The productivity of industries was measured by stochastic parametric frontier functions, in order to measure the efficiency of R&D and accumulation of capital on labor productivity.

The main results highlight the impact of corporate R&D on productivity of high-tech industries, but for other industries those relations are not clear. However, capital accumulation became crucial on low technology to improve their performance. These results, although needing to include a more extensive data set of industries across countries, refer the need for policy and decision makers to allocate public funds for R&D in high-tech industries, while the investment in capital seems crucial, particularly in low-tech industries to improve the productivity.

The purpose of this paper is twofold: to investigate the existence of different degrees of returns to scale in low-tech and high-tech manufacturing industries; and to examine whether the degrees of returns to scale change through time.

The empirical investigation implemented in the paper uses data from the EU KLEMS Database, covering a sample of 12 manufacturing industries in 11 OECD countries over the period 1976-2006. The investigation employed two different estimation methods: instrumental variables and system GMM. The robustness of the results was assessed by employing two different specifications of Kaldor-Verdoorn’s Law, by using lags and five-year averages to smooth business-cycle fluctuations, and by dividing the sample into two time periods.

The results reported in the paper provide strong evidence in support of the hypothesis of substantial increasing returns to scale in manufacturing. The investigation suggests that high-tech manufacturing industries exhibit larger degrees of returns to scale than low-tech manufacturing industries. Finally, the analysis revealed also that the magnitude of the returns to scale in manufacturing have increased in the last decades, driven by increases in the magnitude of returns to scale observed in high-tech industries.

No previous work has assessed the hypothesis that increasing returns to scale vary according to the technological content of industries. Moreover, no previous work has used system GMM or data from EU KLEMS to test Kaldor-Verdoorn’s Law. Most importantly, the findings of the paper present new evidence on the degree of returns to scale in high-tech and low-tech manufacturing industries.

The purpose of this study is to examine the new product management practices adopted by low‐tech small and medium‐sized enterprises (SMEs) in the context of design‐intensive products.

The results are based on a multi‐case comparative setting covering SMEs in furniture industry in three countries, Italy, Spain and Finland.

The study shows considerable differences in performance that occur in terms of the degree of design and innovation, goal orientation and the systematics by which a single furniture business is managed. Proactiveness and freedom in design and innovation together with systematic new product development (NPD) and goal orientation enhances NPD performance. As to the new product uniqueness, innovative design is applicable in furniture industry much as in a similar way as new technological knowledge is in technology industries. The management education and/or interests which are closely related to furniture design and decoration have a clear impact on the level up to which innovative designs are implemented.

The study has direct implications for furniture companies aiming at improving their competitiveness and NPD effectiveness. The study points out the importance of creating a proper innovative culture and being open to new ideas if export markets are targeted.

Technology intensive products in large‐ and medium‐sized companies have been the main focus of NPD performance research, also facing the risk of over‐generalization due to cross‐industry approaches. Low‐tech industries however play a major role as to national income and employment. In this respect the present study aims to highlight the prevailing NPD practices in small design‐intensive firms in the furniture industry by reporting any management gaps which may occur in terms of new product performance.