In this task, you're given a paragraph from the research paper and your task is to generate a suitable title for the research paper based on the given paper. Under 100 words is a good title length.
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Question: Business integration is an important determinant of business value and firm performance which enables a firm to respond to pressing competitive forces. Given its significant role, business integration in general has been an enduring research topic over the years. However, due to its complex nature, the ways to achieve this important organisational capability in firms are still not clearly understood. Hence, the present study is an attempt to 'open this black box' and examine the relevant issues through a case study of M.com, one of the largest e-commerce firms in China. The findings of this case study emphasise that the ability to coordinate organisational interdependencies and asset orchestration according to the needs of business processes is challenging but necessary in order to achieve business integration in e-commerce firms. It is found that asset orchestration in a firm is contingent on the organisational interdependencies such that different interdependence types demand different coordination methods at the very least. It also shows the significant role of IT in asset orchestration and the coordination of organisational interdependencies during business integration. With these capabilities, firms can experience growth and improve the quality of conformance to customer needs.

Answer: The role of organisational interdependencies and asset orchestration in business integration: A case study of M.com


Question: The severe acute respiratory syndrome (SARS) crisis led to the construction of a negative pressure operating theatre at a hospital in Hong Kong. It is currently used for treatment of suspected or confirmed airborne infection cases, and was built in anticipation of a return of SARS, an outbreak of avian influenza or other respiratory epidemics. This article describes the physical conversion of a standard positive pressure operating theatre into a negative pressure environment, problems encountered, airflow design, and evaluation of performance. Since entering regular service, routine measurements and observations have indicated that the airflow performance has been satisfactory. This has also been confirmed by regular air sampling checks. Computational fluid dynamics, a computer modelling technique, was used to compare the distribution of room air before and after the design changes from positive to negative pressure. The simulation results show that the physical environment and the dispersion pattern of bacteria in the negative pressure theatre were as good as, if not better than, those in the original positive pressure design.

Answer: Conversion of operating theatre from positive to negative pressure environment


Question: Confirmed infection cases in mainland China were analyzed using the data up to January 28, 2020 (first 13 days of reliable confirmed cases). For the first period the cumulative number of cases followed an exponential function. However, from January 28, we discerned a downward deviation from the exponential growth. This slower-than-exponential growth was also confirmed by a steady decline of the effective reproduction number. A backtrend analysis suggested the original basic reproduction number R0 to be about 2.4 to 2.5. As data become available, we subsequently analyzed them during three consecutive periods obtaining a sequence of model predictions. All available data up were processed the same way. We used a simple logistic growth model that fitted very well with all data. Using this model and the three sets of data, we estimated maximum cases as about 21,000, 28,000 and 35,000 cases refining these predictions in near-real time. With slightly different approach (linearization in time) the estimate of maximum cases was even higher (about 65,000). Although the estimates of maximum cases increase as more data were reported all models show reaching a peak in mid-February in contrast to the unconfined exponential growth. These predictions do not account for any possible other secondary sources of infection.

Answer:
Forecasting the Wuhan coronavirus (2019-nCoV) epidemics using a simple (simplistic) model