Sources of Information as Determinants of Product and Process Innovation

Strategy and additive technologies as the catalyst for outsourcing, process innovation and operational effectiveness

PURPOSE

There is rising interest in Industry 4.0 as a factor in the competitiveness of the organization. Although many companies are aware of the importance of Industry 4.0, the development of such initiatives in Colombia is slow. Consequently, this research investigates the impact of additive technologies as part of the Industry 4.0 concept on operational effectiveness and, therefore, the competitiveness of the organization and tries to establish the factors that hinder the adequate implementation of such new, innovative technologies.

DESIGN/METHODOLOGY/APPROACH

Structural equation modeling was used to analyze the antecedents and outcomes of operational effectiveness. To this end, 946 usable questionnaires were collected from managers and personnel from Colombian organizations.

FINDINGS

Initial findings show that management is aware of Industry 4.0 concepts and implements strategies for such initiatives. Nevertheless, neither process innovation nor additive technologies have a significant impact on operational effectiveness and therefore on the competitiveness of the organization.

PRACTICAL IMPLICATIONS

The implementation of new innovative technologies requires the closure of the digital gap between urban and rural areas and between large and medium and small enterprises. Similarly, the concept of Industry 4.0 as a new, innovative manufacturing concept requires a transversal implementation to increase the competitiveness of the organization.

ORIGINALITY/VALUE

The value of this paper lies in discussing the current technological and human capabilities and strategies that Colombian organizations, as an example of a developing nation, should improve to leverage the benefits of Industry 4.0 to remain competitive. The results are probably generalizable to other regions in developing countries throughout the world.

Incidence of Human Capital in the Innovative Performance of Service Companies: A Study in Ecuador

The relationship between human capital and innovative performance in service companies has been studied in countries with fast-growing economies and knowledge-intensive companies, but little evidence exists in other contexts. The research examined the relationship between human capital variables and the innovative performance of Ecuadorian service companies. The methodology is quantitative. It is a non-experimental, cross-sectional investigation, and data from Ecuador from the national survey of innovation activities 2015 were used. A bivariate probit regression was performed. The results indicate that the variable training in innovation activities is positively related to service innovation, but not to process innovation, because service innovation requires a greater development of skills and abilities than process innovation in these activities. Company workers and the variable workers with higher education are positively related to process innovation, but not to service innovation. The research contributes to the gap in the literature on the relationship between human capital variables and innovative performance and provides empirical evidence of the relationship in developing countries where evidence is scarce. The research has practical implications for managers and administrators of service companies: Increasing training in innovation activities can increase the potential for service innovation and increasing workers with higher education increases the innovative potential in the processes in these companies. The originality of this study is that it presents evidence of this relationship in a developing country that has companies in a different context such as scarcity of qualified human resources, low level of R&D investment, and companies with a medium level of knowledge complexity, since the evidence focuses on companies in developed countries and knowledge-intensive companies.

The Relationship between Human-Capital Variables and Innovative Performance: Evidence from Colombia

The objective of the research is to analyze the variables of human capital and its relationship with innovation in manufacturing companies. The study is quantitative, nonexperimental, cross-sectional data obtained from Colombia, an emerging country, collected from a national survey of innovation activities. The regression used is bivariate probit. The findings show that the variable “R&D workers” is related to product and process innovation and the variable “workers with higher education” is related to product innovation. The theoretical implication is that the “R&D workers” variable is an important predictor for product and process innovation because it represents skills, abilities, and worker’s experience, and enables finding new uses for knowledge or combining knowledge to achieve innovation. Furthermore, in these companies, the human capital acquired through education develops the skills and abilities that enable product innovation to be achieved, while low investment in training means that the skills achieved by this means are not significant for innovation. The study has practical implications for managers in emerging countries who want to increase the companies’ innovative potential by increasing investment in education and training of its workers.

Barriers to Innovations and Innovative Performance of Companies: A Study from Ecuador

This research aimed to examine the relationship between the barriers to the development of innovation and innovative performance. This is a quantitative, not experimental, cross-sectional research, and the National Survey of Innovation Activities of Ecuador is used. Bivariate Probit regression was used to process the data. The results show empirical evidence that Ecuadorian companies have a great number of barriers to innovation. The main barriers to product innovation and process innovation are as follows: lack of company funds, high costs of innovation, and lack of qualified personnel in the company and the country. In addition product innovation is affected by the lack of market information, and process innovation is affected by the lack of financing from external sources, lack of information on technology, and a market dominated by established companies. The research has theoretical implications because it contributes empirical evidence on the relationship between innovation barriers and innovative performance in developing countries where evidence is scarce. The research has practical implications because it serves as a basis for forming public policies. Business managers and administrators can improve innovative performance by minimizing the impact of the main barriers to innovation.

Operating Successfully on a New Technological Path: The Effect of External Search

Switching to a new technological path is often a serious economic challenge for companies. Incumbents, in particular, are often led by their organizational routines, traditional technological orientation, and experience, and run the risk of losing contact with new technologies, which can decrease their competitiveness. We analyze whether opening up the innovation process to external knowledge partners can help to overcome such path dependence and enable firms to operate successfully on a new technological path. We develop a theoretical concept that shows the potential of external knowledge sources for operating successfully on a new technological path and test it empirically using the example of green technologies. Green technologies are not only relevant for addressing the current environmental problems, but they are also an example of a new technological path that is proving difficult for companies to switch to. Overall, we find strong direct effects of external (green) knowledge on green innovation success. The results even indicate that the direct effect of external knowledge tends to be larger for green than for non-green innovation.

EMPLOYEES’ HETEROGENEOUS QUALIFICATIONS, INTERNAL R&D, FORMAL TRAINING AND INNOVATION PERFORMANCE

The capacity to generate knowledge and disseminate it in a firm is considered a primary strategic competence to attaining competitive gain. Knowledge literature reveals how relevant it is to increase interest in recognising and managing knowledge inherent in workforces and other firms’ dynamic capabilities that allow them to benefit from available knowledge within the milieu. In recognising the importance of employees’ baggage of knowledge, this study uses the Nigerian Innovation Survey for two waves, 2008 and 2010 of 1359 repeated observations of firms in the manufacturing and service sectors to examine the interplay of firms’ dynamic capabilities and innovation optimisation. It uses a recursive bivariate probit and a Tobit model for the estimations. Evidence shows that PhD, BSc, and Diploma associate positively and significantly with the ability of the firms to introduce product innovation. On the moderating estimations, there are mixed results regarding the blend of employees’ qualifications, training and internal R&D. Based on this, while the share of the highly educated workforce supports the introduction of product innovation, it does not, however, substantially increase the probability of firm-level innovativeness regarding process innovation. Although the blend of BSc and HND with R&D supports the propensity to introduce process innovation, it results in a decline with Diploma and R&D. The implication of the results offers management some investment choices on the initiation of formal training and the management of internal R&D through employees’ expertise. This study contributes to the existing literature on the relevance of employees’ different degrees being reinforced by training and internal R&D being boosted by employees’ qualifications in supporting the development of product and process innovation.

Can we predict firms' innovativeness? The identification of innovation performers in an Italian region through a supervised learning approach

The study shows the feasibility of predicting firms' expenditures in innovation, as reported in the Community Innovation Survey, applying a supervised machine-learning approach on a sample of Italian firms. Using an integrated dataset of administrative records and balance sheet data, designed to include all informative variables related to innovation but also easily accessible for most of the cohort, random forest algorithm is implemented to obtain a classification model aimed to identify firms that are potential innovation performers. The performance of the classifier, estimated in terms of AUC, is 0.794. Although innovation investments do not always result in patenting, the model is able to identify 71.92% of firms with patents. More encouraging results emerge from the analysis of the inner working of the model: predictors identified as most important-such as firm size, sector belonging and investment in intangible assets-confirm previous findings of literature, but in a completely different framework. The outcomes of this study are considered relevant for both economic analysts, because it demonstrates the potential of data-driven models for understanding the nature of innovation behaviour, and practitioners, such as policymakers or venture capitalists, who can benefit by evidence-based tools in the decision-making process.

Modelling innovation performance of European regions using multi-output neural networks

Regional innovation performance is an important indicator for decision-making regarding the implementation of policies intended to support innovation. However, patterns in regional innovation structures are becoming increasingly diverse, complex and nonlinear. To address these issues, this study aims to develop a model based on a multi-output neural network. Both intra- and inter-regional determinants of innovation performance are empirically investigated using data from the 4^th and 5^th Community Innovation Surveys of NUTS 2 (Nomenclature of Territorial Units for Statistics) regions. The results suggest that specific innovation strategies must be developed based on the current state of input attributes in the region. Thus, it is possible to develop appropriate strategies and targeted interventions to improve regional innovation performance. We demonstrate that support of entrepreneurship is an effective instrument of innovation policy. We also provide empirical support that both business and government R&D activity have a sigmoidal effect, implying that the most effective R&D support should be directed to regions with below-average and average R&D activity. We further show that the multi-output neural network outperforms traditional statistical and machine learning regression models. In general, therefore, it seems that the proposed model can effectively reflect both the multiple-output nature of innovation performance and the interdependency of the output attributes.

Optimization of a Multi-Product Intra-Supply Chain System with Failure in Rework

Globalization has created tremendous opportunities, but also made business environment highly competitive and turbulent. To gain competitive advantage, management of present-day transnational firms always seeks options to trim down various transaction and coordination costs, especially in the area of controllable intra-supply chain system. This study investigates a multi–product intra-supply chain system with failure in rework. To achieve maximum machine utilization, multiple products are fabricated in succession on a single machine. During the process, production of some defective items is inevitable. Reworking of nonconforming items is used to reduce the quality cost in production and achieving the goal of lower overall production cost. Because reworks are sometimes unsuccessful, failures in rework are also considered in this study. Finished goods for each product are transported to the sales offices when the entire production lot is quality assured after rework. A multi-delivery policy is used, wherein fixed quantity n installments of the finished lot are transported at fixed intervals during delivery time. The objective is to jointly determine the common production cycle time and the number of deliveries needed to minimize the long–term expected production–inventory–delivery costs for the problem. With the help of a mathematical model along with optimization technique, the optimal production–shipment policy is obtained. We have used a numerical example to demonstrate applicability of the result of our research.