Product and Process Innovation in a Growth Model of Firm Selection

Recent empirical evidence based on firm level data emphasizes firm heterogeneity in innovation activities and the different effects of process and product innovations on the productivity level and productivity growth. To match this evidence, this paper develops an endogenous growth model with two sources of firm heterogeneity: production efficiency and product quality.Both attributes evolve endogenously through firms’ innovation choices. Growth is driven by innovation and self-selection of firms and sustained by entrants who imitate incumbents. Calibrating the economy to match the Spanish manufacturing sector, the model enables to quantify the different effects of selection, innovation, and imitation as well as product and process innovation on growth. Compared to single attribute models of firm heterogeneity, the model provides a more complete characterization of firms’ innovation choices explaining the partition of firms along different innovation strategies and generating consistent firm size distributions.endogenous growth theory, firm dynamics, heterogeneous firms, productivity, quality, innovation

Novelty of product innovation : the role of different networks

In the current competitive scenario, firms are driven to introduce products with a higher degree of novelty. Consequently, there is a growing need to understand the critical success factors behind radical innovation. Specifically, this work empirically and theoretically analyses the role of different types of collaborative networks in achieving product innovation and, more precisely, the degree of novelty. Using a longitudinal data of Spanish manufacturing firms, our results show that the continuity on the co-operative strategy, the type of partner and the diversity of collaborative networks are critical factors in achieving a higher degree of novelty in product innovatio

NOVELTY OF PRODUCT INNOVATION: THE ROLE OF DIFFERENT NETWORKS

In the current competitive scenario, firms are driven to introduce products with a higher degree of novelty. Consequently, there is a growing need to understand the critical success factors behind radical innovation. Specifically, this work empirically and theoretically analyses the role of different types of collaborative networks in achieving product innovation and, more precisely, the degree of novelty. Using a longitudinal data of Spanish manufacturing firms, our results show that the continuity on the co-operative strategy, the type of partner and the diversity of collaborative networks are critical factors in achieving a higher degree of novelty in product innovation.

Consumer-driven innovation networks and e-business management systems

This paper examines the use of consumer-driven innovation networks within the UK food-retailing industry using qualitative interview-based research analysed within an economic framework. This perspective revealed that, by exploiting information gathered directly from their customers at point-of-sale and data mining, supermarkets are able to identify consumer preferences and co-ordinate new product development via innovation networks. This has been made possible through their information control of the supply-chain established through the use of transparent inventory management systems. As a result, supermarkets’ e-business systems have established new competitive processes in the UK food-processing and retailing industry and are an example of consumer-driven innovation networks. The informant-based qualitative approach also revealed that trust-based transacting relationships operated differently from those previously described in the literature

Data-driven through-life costing to support product lifecycle management solutions in innovative product development

Innovative product usually refers to product that comprises of creativity and new ideas. In the development of such a new product, there is often a lack of historical knowledge and data available to be used to perform cost estimation accurately. This is due to the fact that traditional cost estimation methods are used to predict costs only after a product model has been built, and not at an early design stage when there is little data and information available. In light of this, original equipment manufacturers are also facing critical challenges of becoming globally competitive and increasing demands from customer for continuous innovation. To alleviate these situations this research has identified a new approach to cost modelling with the inclusion of product lifecycle management solutions to address innovative product development.The aim of this paper, therefore, is to discuss methods of developing an extended-enterprise data-driven through-life cost estimating method for innovative product development

Data-Driven Transformation in the Automotive Industry: The Role of Customer Usage Data in Product Development

Automotive manufacturers are pressured to integrate customers into product development effectively to foster innovation and remain competitive. While traditional approaches to customer integration have relied on market research and the customer’s intention to use, the digital transformation of the automotive industry increasingly enables manufacturers to leverage customer usage data for product development. However, we lack insights into how customer data influences automotive productive development. To close this gap, we investigated the role of customer usage data for product development at a leading car manufacturer. Drawing on 20 expert interviews, we derived three key dimensions that explain how customer usage data influence product development in automotive: “data-driven product evaluation,” “data-driven product development,” and “data-driven product innovations.” Our findings shed light on the transformative role of customer usage data for product development and provide valuable guidance for practitioners to effectively leverage customer usage data as part of the automotive digital transformation

Assessing the influence of emerging technologies on organizational data driven culture and innovation capabilities: A sustainability performance perspective

Industry 4.0 applications can accelerate data driven decision making culture in organizations. Such data driven culture can have a profound impact on the organizational capabilities underlying product and process innovation. While there is a relatively developed body of literature on the effect of data driven culture on organizational performance, there is virtually no study that has examined how Industry 4.0 influences the data driven culture of organizations and how in its turn such culture influences both product and process innovation. Furthermore, the role of organizational data driven culture has seldom been examined in relation to organizational sustainability performance. Against this backdrop, the aim of this study is to examine the role of emerging Industry 4.0 technologies on the data driven culture of organizations and analyze if and how such data driven culture influences organizational performance ultimately translating into competitive advantage. By leveraging the Resource Based View (RBV) and Dynamic Capabilities theory, we developed a theoretical model and tested it using a PLS-SEM approach on a sample of 416 organizations. We found that adoption of industry 4.0 technologies influences organizational performance by improving social, competitive, and financial performance of the organizations relying on data driven culture and improved innovative capabilities

Data-Driven, Data-Informed, Data-Augmented: How Ubisoft’s Ghost Recon Wildlands Live Unit Uses Data for Continuous Product Innovation

To stay ahead of the competition, firms must continuously learn from their customers and swiftly adopt those lessons to improve their products. A unit at Ubisoft, a leading game publisher headquartered in Paris, has established a three-pronged approach to drive product innovation based on three practices: data-driven exploration, data-augmented ideation, and data-informed validation. By establishing processes and capabilities for these practices and blending them in a portfolio approach to product design, they maximize the value generation potential of the data at their disposal. Product development in a variety of industries can benefit from the lessons of these data-oriented innovation practices

Understanding the Impact of Business Analytics on Innovation

The advances in Big Data and Business Analytics (BA) have provided unprecedented opportunities for organizations to innovate. With new and unique insights gained from BA, companies are able to develop new or improve existing products/services. However, few studies have investigated the mechanism through which BA contributes to a firm’s innovation success. This research aims to address this gap. From an information processing and use perspective, a research model is proposed and empirically validated with data collected from a survey with UK businesses. The evidence from the survey of 296 respondents supports the research model that provides a focused and validated view on BA’s contribution to innovation. The key findings suggest that BA directly improves environmental scanning which in turn helps to enhance a company’s innovation in terms of new product novelty and meaningfulness. However, the effect of BA’s contribution would be increased through the mediation role of data-driven culture in the organization. Data-driven culture directly impacts on new product novelty, but indirectly on product meaningfulness through environmental scanning. The findings also confirm that environmental scanning directly contributes to new product novelty and meaningfulness which in turn enhance competitive advantage. The model testing results also reveal that innovation success can be influenced by many other factors which should be addressed alongside the BA applications

Dynamic supply chain decisions based on networked sensor data:An application in the chilled food retail chain

With large volume of product flows and complex supply chain processes, more data than ever before is being generated and collected in supply chains through various tracking and sensory technologies. The purpose of this study is to show a potential scenario of using a prototype tracking tool that facilitate the utilisation of sensor data, which is often unstructured and enormous in nature, to support supply chain decisions. The research investigates the potential benefits of the chilled food chain management innovation through sensor data driven pricing decisions. Data generated and recorded through the sensor network are used to predict the remaining shelf-life of perishable foods. Numerical analysis is conducted to examine the benefit of proposed approach under various operational situations and product features. The research findings demonstrate a way of modelling pricing and potential of performance improvement in chilled food chains to provide a vision of smooth transfer and implementation of the sensor data driven supply chain management. The research finding would encourage firms in the food industry to explore innovation opportunities from big data and develop proper data driven strategies to improve their competitiveness