Social media and sensemaking patterns in new product development: demystifying the customer sentiment

Artificial intelligence by principle is developed to assist but also support decision making processes. In our study, we explore how information retrieved from social media can assist decision-making processes for new product development (NPD). We focus on consumers’ emotions that are expressed through social media and analyse the variations of their sentiments in all the stages of NPD. We collect data from Twitter that reveal consumers’ appreciation of aspects of the design of a newly launched model of an innovative automotive company. We adopt the sensemaking approach coupled with the use of fuzzy logic for text mining. This combinatory methodological approach enables us to retrieve consensus from the data and to explore the variations of sentiments of the customers about the product and define the polarity of these emotions for each of the NPD stages. The analysis identifies sensemaking patterns in Twitter data and explains the NPD process and the associated steps where the social interactions from customers can have an iterative role. We conclude the paper by outlining an agenda for future research in the NPD process and the role of the customer opinion through sensemaking mechanisms

The role of iron in phytoplankton photosynthesis, and the potential for iron-limitation of primary productivity in the sea

Iron supply has been suggested to influence phytoplankton biomass, growth rate and species composition, as well as primary productivity in both high and low NO3− surface waters. Recent investigations in the equatorial Pacific suggest that no single factor regulates primary productivity. Rather, an interplay of bottom-up (i.e., ecophysiological) and top-down (i.e., ecological) factors appear to control species composition and growth rates. One goal of biological oceanography is to isolate the effects of single factors from this multiplicity of interactions, and to identify the factors with a disproportionate impact. Unfortunately, our tools, with several notable exceptions, have been largely inadequate to the task. In particular, the standard technique of nutrient addition bioassays cannot be undertaken without introducing artifacts. These so-called ‘bottle effects’ include reducing turbulence, isolating the enclosed sample from nutrient resupply and grazing, trapping the isolated sample at a fixed position within the water column and thus removing it from vertical movement through a light gradient, and exposing the sample to potentially stimulatory or inhibitory substances on the enclosure walls. The problem faced by all users of enrichment experiments is to separate the effects of controlled nutrient additions from uncontrolled changes in other environmental and ecological factors. To overcome these limitations, oceanographers have sought physiological or molecular indices to diagnose nutrient limitation in natural samples. These indices are often based on reductions in the abundance of photosynthetic and other catalysts, or on changes in the efficiency of these catalysts. Reductions in photosynthetic efficiency often accompany nutrient limitation either because of accumulation of damage, or impairment of the ability to synthesize fully functional macromolecular assemblages. Many catalysts involved in electron transfer and reductive biosyntheses contain iron, and the abundances of most of these catalysts decline under iron-limited conditions. Reductions of ferredoxin or cytochrome f content, nitrate assimilation rates, and dinitrogen fixation rates are amongst the diagnostics that have been used to infer iron limitation in some marine systems. An alternative approach to diagnosing iron-limitation uses molecules whose abundance increases in response to iron-limitation. These include cell surface iron-transport proteins, and the electron transfer protein flavodoxin which replaces the Fe-S protein ferredoxin in many Fe-deficient algae and cyanobacteria