Search results

The purpose of this paper is to suggest how firms could use big data to facilitate product innovation processes, by shortening the time to market, improving customers’ product adoption and reducing costs.

The research is based on a two-step approach. First, this research identifies four potential key success factors for organisations to integrate big data in accelerating their product innovation processes. The proposed factors are further examined and developed by conducting interviews with different organisation experts and academic researchers. Then a framework is developed based on the interview outputs. The framework sets out the key success factors involved in leveraging big data to reduce lead times and costs in product innovation processes.

The three determined key success factors are: accelerated innovation process; customer connection; and an ecosystem of innovation. The authors believe that the developed framework based on big data represents a paradigm shift. It can help firms to make new product development dramatically faster and less costly.

The proposed accelerated innovation processes demand a shift in traditional organisational culture and practices. It is, though, meaningful only for products and services with short life cycles. Moreover, the framework has not yet been widely tested.

This paper points to the vital role of big data in helping firms to accelerate product innovation processes. First of all, it allows organisations to launch new products to market as quickly as possible. Second, it helps organisations to determine the weaknesses of the product earlier in the development cycle. Third, it allows functionalities to be added to a product that customers are willing to pay a premium for, while eliminating features they do not want. Last, but not least, it identifies and then prioritises customer needs for specific markets.

The research shows that firms could harvest external knowledge and import ideas across organisational boundaries. An accelerated innovation process based on big data is characterised by a multidimensional process involving intelligence efforts, relentless data collection and flexible working relationships with team members.

Assessing a measure of sustainable supply chain management (SSCM) performance is currently a key challenge. The literature on SSCM is very limited and performance measures need to have a systematic framework. The recently developed balanced scorecard (BSC) is a measurement system that requires a balanced set of financial and non-financial measures. The purpose of this paper is to evaluate the SSCM performance based on four aspects i.e. sustainability, internal operations, learning and growth, and stakeholder.

This paper developed a BSC hierarchical network for SSCM in a close-loop hierarchical structure. A generalized quantitative evaluation model based on the Fuzzy Delphi Method (FDM) and Analytical Network Process (ANP) were then used to consider both the interdependence among measures and the fuzziness of subjective measures in SSCM.

The results of this study indicate that the top-ranking aspect to consider is that of stakeholders, and the top five criteria are green design, corporate sustainability, strategic planning for environmental management, supplier cost-saving initiatives and market share.

The main contributions of this study are twofold. First, this paper provides valuable support for supply chain stakeholders regarding the nature of network hierarchical relations with qualitative and quantitative scales. Second, this paper improves practical performance and enhances management effectiveness for SSCM.

The purpose of this paper is to enhance the understanding of sustainable supply chain management (SSCM) and provide a comprehensive and quantitative method to assess performance.

The study applied interval-valued triangular fuzzy numbers associated with grey relational analysis to improve the insufficient information and overcome the incomplete system under uncertainty.

The findings support the argument that the triple bottom line is insufficient to cover the entire concept of SSCM; in particular, the aspects of operations, stakeholders and resilience have not been addressed in previous studies.

The results reveal that the triple bottom line concept is insufficient to illustrate the principles of SSCM and to provide an extensive basis for theory development. The aspects and criteria considered in the study only relate to the studied company and may need to be reviewed when applied to other industries.

The methodology and findings of the study demonstrate the core applications of criteria ranking and identify priority areas that utilize less investment but that may maintain the studied company’s current performance. Suggestions for the prioritization of criteria to enhance SSCM performance are provided.

The present study provides three valuable contributions. First, it adopts collaboration theory to furnish a theoretical foundation for SSCM. Second, the proposed hybrid method is able to overcome uncertainty and subsequently evaluate SSCM while utilizing incomplete and imprecise information. Third, the evaluation provides significant results for consideration in decision making by the studied company.

Big data analytics (BDA) gets all the attention these days, but as important—and perhaps even more important—is big data analytics quality (BDAQ). Although many companies realize a full return from BDA, others clearly struggle. It appears that quality dynamics and their holistic impact on firm performance are unresolved in data economy. The purpose of this paper is to draw on the resource-based view and information systems quality to develop a BDAQ model and measure its impact on firm performance.

The study uses an online survey to collect data from 150 panel members in France from a leading market research firm. The participants in the study were business analysts and IT managers with analytics experience.

The study confirms that perceived technology, talent and information quality are significant determinants of BDAQ. It also identifies that alignment between analytics quality and firm strategy moderates the relationship between BDAQ and firm performance.

The findings inform practitioners that BDAQ is a hierarchical, multi-dimensional and context-specific model.

The study advances theoretical understanding of the relationship between BDAQ and firm performance under the influence of firm strategy alignment.