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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 customersâ€…
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 customersâ€™ 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.
Existing robot-assisted minimally invasive surgery (RMIS) system lacks of force feedback, and it cannot provide the surgeon with interaction forces between the surgical…
Existing robot-assisted minimally invasive surgery (RMIS) system lacks of force feedback, and it cannot provide the surgeon with interaction forces between the surgical instruments and patientâ€™s tissues. This paper aims to restore force sensation for the RMIS system and evaluate effect of force sensing in a master-slave manner.
This paper presents a four-DOF surgical instrument with modular joints and six-axis force sensing capability and proposes an incremental position mode masterâ€“slave control strategy based on separated position and orientation to reflect motion of the end of master manipulator to the end of surgical instrument. Ex-vivo experiments including tissue palpation and blunt dissection are conducted to verify the effect of force sensing for the surgical instrument. An experiment of trajectory tracking is carried out to test precision of the control strategy.
Results of trajectory tracking experiment show that this control strategy can precisely reflect the hand motion of the operator, and the results of the ex-vivo experiments including tissue palpation and blunt dissection illustrate that this surgical instrument can measure the six-axis interaction forces successfully for the RMIS.
This paper addresses the important role of force sensing and force feedback in RMIS, clarifies the feasibility to apply this instrument prototype in RMIS for force sensing and provides technical support of force feedback for further clinical application.
Big data analytics (BDA) gets all the attention these days, but as importantâ€”and perhaps even more importantâ€”is big data analytics quality (BDAQ). Although many companies…
Big data analytics (BDA) gets all the attention these days, but as importantâ€”and perhaps even more importantâ€”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.