The New Conglomerates and the Ecosystem Advantage

It is the purpose of this paper to explore the emergence of a new form of web-based company and how the new organisational form is able to access multiple-markets and industries by exploiting the benefits of a platform ecosystem business model. Internet-based companies such as Amazon, Google, Facebook and Apple were referred to as the “Gang of Four” in May, 2011, by Eric Schmidt, Executive Chairman of Google. These twenty first century companies are leading an Internet-based consumer revolution which is having a disruptive impact on a broad range of industries and markets. Using their platform-based ecosystems (Gawar, 2009), the Internet-based corporations are able to leverage these core competencies and enter industries that appear unrelated to their core businesses. This has given rise to a new form of conglomerate business model that contrasts with the Western industrial conglomerates of the 1960s and early 1970s. Most of these corporations were broken-up in the early 1980s and the remaining core businesses resorted to focused strategies. The paper also analyses why the new Internet-based firms do not follow the positioning school of strategy and seek monopolistic rents – profits arising from market power - (Porter, 1979) by locating in attractive industries. Instead, these firms leverage core competencies and achieve Ricardian economic rents (Grant, 2008) – profits arising from superior resources - through strategies of stretch and leverage (Prahalad and Hamel, 1990). By adopting a core `competency tree` configuration (Prahalad and Hamel, 1990), a core competency platform is leveraged which allows the Internet-based firms to enter multiple markets using asset-light business models. This new form of competitive advantage is based on having a superior ecosystem that uses data as the key resource instead of capital; capital being the main resource underpinning the success of the traditional Western conglomerates of the 1960s and early 1970s

Spatial and Temporal Variance of Bi-hourly Grass Pollen Concentrations in the Local Surroundings of Worcester, UK

B: Grass pollen is the most important aeroallergen worldwide and the health outcome among sensitive individuals is closely related to exposure. It has been argued that grass pollen concentrations can be expected to vary substantially within the urban environment, partly due to source distribution and partly due to dispersion and deposition mechanisms. Most studies conducted on local spatial and temporal variance of pollen concentrations are from one season. Only a few studies include multiple seasons and the results are inconclusive. The patterns and factors responsible for local spatial and temporal pollen variance are still largely unknown. Bi-hourly pollen data provides finer temporal resolution than the standardized daily data otherwise used. Bi-hourly data collected from two sampling sites are used to investigate local spatial and temporal patterns of grass pollen concentrations in Worcester. M: Grass pollen was sampled from two locations in Worcester during the years 2016 and 2017 using a Burkard sampler. Daily and bi-hourly grass pollen concentrations were investigated for temporal and spatial variance using statistical methods by comparing years and locations. The investigation is being repeated for the year 2018. R: Preliminary results from 2016 and 2017 suggest that there is a high spatial correlation for the bi-hourly concentrations in 2016 but not in 2017. In 2016, all recorded peaks except one coincide with a corresponding peak. In 2017, the highest peak coincided with a corresponding peak but the rest of the data did not. Results from 2018 are currently unknown. C: Spatial and temporal variance in grass pollen concentrations fluctuates between years and locations. Peak concentrations tend to have the highest correlations compared to low concentrations. The results show that at least two years of data are needed to establish potential autocorrelation between nearby sites. Future work needs to include longer time-series, more locations and local grass source maps to understand key underlying factors of localized grass pollen concentrations