A framework for accelerated product innovation in a big data environment

This dissertation is concerned with the best approaches for accelerated product innovation in a big data environment. It describes the development and examining of a framework consisting of three sets of different phases to support managers to attain accelerated product innovation in high-tech industries. This research also investigates the roles of big data in facilitating new product development, and the factors for successful implementation of big data. Accelerated product innovation has become increasingly important for both theory and practice in today’s rapidly changing business environment. The phenomenon is reinforced by the increasing amounts of data available to business and the associated big data efforts in innovation by new information and communication technologies, as well as by new business models and organisational forms. There are two important issues associated with accelerated product innovation. Firstly, there is an underlying question as to which specific approaches for accelerated product innovation will be successful for a particular company. That is, even as more and more firms begin to acknowledge the significance of accelerated product innovation, they still suffer from a lack of knowledge about how to attain it. Secondly, how do companies apply big data to support accelerated product innovation in new product development? The specific benefits of accelerated product innovation may be summarised as: greater opportunity to incorporate the latest technology; increased market share; higher value; and more accurate forecasts of customer needs. Although previous studies have pointed out that firms can facilitate their product innovation by leveraging the huge potential value of big data, no studies have systematically investigated how firms can apply big data to facilitate accelerated product innovation. The research was carried out in two stages. Stage one proposed a set of approaches for accelerated product innovation based on the literature studies. The approaches identified were categorised into four innovation phases. Then, the phases were refined from empirical research. The refined phases were further examined in three cases to develop a framework. During the second stage, a set of propositions were established according to the best approaches identified from the framework. The propositions were examined in five in-company case studies, in which qualitative data collection was applied. As well as this, the qualitative investigation through multiple case studies of diverse companies were executed to explore and compare key elements of big data in the context of product innovation, and more specifically in different phases of new product development. The primary outcome of this research has been the development and examination of a framework for accelerated product innovation in a big data environment. The approaches identified from the framework demonstrated a high utility in practice. The traditional role of innovation in competitive success has been redefined to reflect a time-based requirement. Accordingly, accelerated innovation is associated with maximisation of the product success rates, higher profitability and competitive advantage. All five companies in the present case study were applying approaches in product development for accelerating NPD, better understanding of customers’ needs, higher revenue growth, and faster launch of new products to market. The empirical findings also show that the role of big data in product innovation is highly dependent on the ability to understand a specific objective or problem, and to examine whether using big data is the right approach for solving that problem. There is a prerequisite for securing distinct resources and organisational capabilities to succeed with implementing big data into new product development. Other important factors that need to be well considered by organisations when forming an implementation strategy are organisations’ data maturity and effective change management, especially if the organisation is utilising more traditional innovation processes. However, novel methods rely heavily on extensive and varied data which translates in an adoption urgency to sustain competitive advantage and secure responsive innovation. The main contributions of this research is that it usefully extends the accelerated product innovation literature by clearly defining the concept of accelerated product innovation, and by developing a conceptual framework with six propositions about how, specifically, big data and ICTs can contribute to accelerated product innovation. Then, it offers qualitative evidence from five case studies involving world-leading firms, and explaining how product innovation can most appropriately be accelerated in a big data environment

An analytic infrastructure for harvesting big data to enhance supply chain performance

Big data has already received a tremendous amount of attention from managers in every industry, policy and decision makers in governments, and researchers in many different areas. However, the current big data analytics have conspicuous limitations, especially when dealing with information silos. In this paper, we synthesise existing researches on big data analytics and propose an integrated infrastructure for breaking down the information silos, in order to enhance supply chain performance. The analytic infrastructure effectively leverages rich big data sources (i.e. databases, social media, mobile and sensor data) and quantifies the related information using various big data analytics. The information generated can be used to identify a required competence set (which refers to a collection of skills and knowledge used for specific problem solving) and to provide roadmaps to firms and managers in generating actionable supply chain strategies, facilitating collaboration between departments, and generating fact-based operational decisions. We showcase the usefulness of the analytic infrastructure by conducting a case study in a world-leading company that produces sports equipment. The results indicate that it enabled managers: (a) to integrate information silos in big data analytics to serve as inputs for new product ideas; (b) to capture and interrelate different competence sets to provide an integrated perspective of the firm’s operations capabilities; and (c) to generate a visual decision path that facilitated decision making regarding how to expand competence sets to support new product development

Managing social responsibility in Chinese agriculture supply chains through the “a company + farmers” model

Purpose:Corporate social responsibility (CSR) has received a large amount of attention in research and in practice. As a response to the growing awareness of and concern about social and environmental issues, an increasing number of companies are integrating their supply chains and building an alliance of “a company + farmers”. The overall research question of this study is derived from the literature and is aimed at identifying factors that influence the integration of the agriculture supply chain and at exploring the relationship between these factors and quality performance. Design/methodology/approach: The analysis is based on questionnaire survey data collected from 462 Chinese farmers under the organization pattern of “a company + farmers”. A structural equation model is applied in the empirical analysis of the relations among trust, relationship commitments of different types (normative and instrumental), supply chain integration and quality performance. Findings: An understanding of the various influences on supply chain integration and quality performance is important in relation to CSR in Chinese agriculture. The results show that supply chain integration has positive effects on quality performance. Moreover, farmers' normative relationship commitment to the company is positively related to supply chain integration. However, farmers’ instrumental relationship commitment to the company does not significantly affect the degree of integration between farmers and companies. Furthermore, trust has positive influences on the two types of relationship commitment and on supply chain integration. Research limitations/implications: The findings provide a theoretical basis and practice guidelines for agricultural enterprises to manage CSR under the pattern of “a company + farmers”. The results help enterprises to acquire detailed information about the entire process of agricultural production, improve the quality and safety of primary agricultural products, and enhance the competitiveness of Chinese agricultural products in the market. Originality/value: The paper shows that enterprises working within Chinese agriculture supply chains have a long tradition of working with CSR and supports cooperation between the European Union and China on food and agriculture

A framework for evaluating the performance of sustainable service supply chain management under uncertainty

Developing and accessing a measure of sustainable service supply chain management (SSSCM) performance is currently a key challenge. The main contributions of this study are two-fold. First, this paper provides valuable support for SSSCM regarding the nature of network hierarchical relations with qualitative and quantitative scales. Second, this study indicates the practical implementation and enhances management effectiveness for SSSCM. The literature on SSSCM is very limited and performance measures need to have a systematic framework. The purpose of this study is to develop and evaluate the SSSCM importance based on aspects i.e., environmentally conscious design, environmental service operations design and environmentally sustainable design. This paper developed a hierarchical network for SSSCM in a closed-loop hierarchical structure. A generalized quantitative evaluation model based on the Fuzzy Delphi Method and Analytical Network Process were then used to consider both the interdependence among measures and the fuzziness of subjective measures in SSSCM. The results indicate that the top-ranking aspect to consider is that of environmental service operation design, and the top criteria is reverse logistics integrated into service packag

Exploring sustainability-oriented innovation capabilities in the Indonesian manufacturing firms, 80th Annual Meeting of the Academy of Management (AOM), Vancouver, Canada (2020).

Although the studies on sustainability-oriented innovation (SOI) have grown significantly in the last decades, to date research on specific SOI capabilities required by the firm to be a more sustainable innovator is still under-explored. Capability-based perspective is revisited to become a foundation for this empirical study. Specifically, capability theories linked to innovation and sustainability fields involved, including innovation management capabilities (IMC), natural resource-based view (NRBV), and social RBV (SRBV) with dynamic capabilities as overarching theory. As the nature of this research is exploratory, a qualitative approach is employed uses semi-structured interviews to 33 owner and manager of manufacturing firms in Indonesia, supplemented by site visit and archival documentation for triangulation. The findings suggested that around half of the firms studied adopting SOI with an operational optimisation approach. It is found from the data that transition is exists between SOI approaches. Firms operating at a higher level of SOI approach have specific dynamics capabilities above baseline ordinary SOI capabilities (production, marketing, environmental and social) that help them become a more sustainable innovator. These SOI dynamics capabilities include capture SOI idea, proactivity to SOI opportunity, mechanism to implement SOI, stakeholder management for SOI, SOI governance, and SOI continual learning

Responsible Research and Innovation (RRI) in Emerging Economies: a Preliminary Review

Interest in responsible research and innovation (RRI) has been increased in recent years in the academic, practical and policy domains. Although initially introduced in Europe and the US, in the subsequent development, attention to RRI grows in another context. The aim of this paper is to provide a preliminary review of the current status of responsible research and innovation (RRI) research, focusing on emerging economy's context. Systematic review method is used for this purpose. The current research on RRI in emerging economies has emerged since 2013, involving multi-disciplinary researchers and has been published in journals from various disciplines. The most discussed dimension is `inclusion' through public engagement in different phase of research and innovation. The dimension of `anticipation' that plays an important role in the early phase of research and innovation has not been much discussed compared to other dimensions

A proposed framework for accelerated innovation in data-driven environments

Purpose In today’s rapidly changing business environment, the case for accelerated innovation processes has become increasingly compelling at both a theoretical and practical level. Thus, the purpose of this paper is to propose a conceptual framework for accelerated innovation in a data-driven market environment. Design/methodology/approach This research is based on a two-step approach. First, a set of propositions concerning the best approaches to accelerated innovation are put forward. Then it offers qualitative evidence from five case studies involving world-leading firms, and explains how innovation can be accelerated in different kinds of data-driven environments. Findings The key sets of factors for accelerated innovation are: collateral structure; customer involvement; and ecosystem of innovation. The proposed framework enables firms to find ways to innovate – specifically, to make product innovation faster and less costly. Research limitations/implications The findings from this research focus on high-tech industries in China. Using several specific innovation projects to represent accelerated innovation could raise the problem of the reliability and validity of the research findings. Additional research will probably be required to adapt the proposed framework to accommodate the cultural nuances of other countries and business environments. Practical implications The study is intended as a framework for managers to apply their resources to conduct product innovation in a fast and effective way. It developed six propositions about how, specifically, data analytics and ICTs can contribute to accelerated innovation. Originality/value The research shows that firms could harvest external knowledge and import ideas across organisational boundaries. An accelerated innovation framework is characterised by a multidimensional process involving intelligence efforts, relentless data collection and flexible working relationships with team members. </jats:sec