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Example of Research Methodology Using Saunders et al. Research Onion Model

The research onion model was suggested by. To aid researchers in developing a technique and constructing a research design in the field of prospective studies. The six key layers of this study onion model act as a step-by-step reference for researchers creating and organizing their research methodology (Melnikovas , 2018) 

To solve the research challenge and based on what has been said about previous studies, the mixed methods analysis approach will be used. as it will enable researchers to begin their work by gaining a thorough understanding of angola’s current status and what ways can be adopted for a sustainable smart urbanisation infrastructure development in angola and its surroundings. it contains both quantitative and qualitative data for study.the quantitative data would be used to count people’s opinions on the housing issues being discussed. however, without deep and rich qualitative evidence, crucial information about people’s views and desires would be lost. the above allows for a deeper level of knowledge that is not possible to access by quantitative data. however, without deep and rich qualitative evidence, crucial information about people’s views and desires would be lost (omotayo , 2017).the above allows for a deeper level of knowledge that is not possible to access by quantitative data. to put it another way, quantitative data will provide valuable numeric measurement data, while qualitative data will provide more nuance facts and hypotheses, as well as define association

 

The Mixed methods methodology

The fusion of conventional quantitative and qualitative methods resulted in a modern process. The mixed methods methodology was born out of the need to help researchers better understand social relations and their complexities by combining quantitative and qualitative analysis methods but still acknowledging their limits (McKim , 2017). In social science, mixed approaches are also noted for the principle of triangulation. Researchers may use triangulation to present different observations about a particular problem by integrating elements of quantitative and qualitative methods in a single sample.

Mixed Methods: Characteristics to Look For

Quantitative and qualitative evidence are used (e.g., numeric scores, open and closed-ended questions etc.).  Depending on the configuration, data may be obtained simultaneously or sequentially. Any data form should be given priority, or both can be handled equally. Allows researchers to converge or validate    conclusions by expanding their interpretation from one approach to another. Quantitative analysis provides a broad level of generalisation, while qualitative research provides a deep level of thorough interpretation (Fetters , et al 2019).

Four key factors influence the type of mixed-methods approach used

Interactions:

A strand is a fundamental aspect of a design that encompasses either the qualitative or quantitative component. Interactive – one strand’s method or results can influence the second strand’s process or results (e.g., the quantitative strand’s findings can influence how the qualitative strand is conducted). Interaction will happen at any time during the research.

Independent Separate strands (e.g., separate testing queries, data collection, etc.) Only at the end of the analysis, during perception, can the strands communicate (McKim , 2017)

Priority

Quantitative – the quantitative strand receives more consideration; 

qualitative – the qualitative strand receives more attention.

 .Equal – both the quantitative and qualitative strands make an equal contribution.

 Timing (i.e., the order of our strands)

         Quantitative first.

         Qualitative first.

         Concurrent

Mixing: is the process of combining data in order to address testing questions.

         During data collection. 

         During interpretation.

The Mixed Research methods gives:

Helps researchers to answer exploratory and confirmatory study questions.

Includes searching at situations from different perspectives and reacting to study questions. Although this can be inconvenient when divergent conclusions necessitate re-examining theories, it can also lead to more formed conclusions and more conceiving explanations and hypotheses.

It comes up with more thorough conclusions. It helps to eliminate some of the flaws that some approaches have by combining them in a manner that has complementary strengths and non-overlapping weaknesses.

Research Philosophy

 (Timans , et al 2019) discuss mixed methods in the contexts of the philosophy of science. Epistemologically, pragmatism is premised on the idea that research can steer clear of metaphysical debates about the nature of truth and reality and focus instead on ‘practical understandings’ of concrete, real-world issues. While this approach is compatible with qualitative-dominant interpretivist understandings of socially constructed reality, the emphasis is on interrogating the value and meaning of research data through examination of its practical consequences. 

Grounded theory 

Grounded theory methods have earned their place as a standard social research method and have influenced researchers from varied disciplines and professions. Yet grounded theory continues to be a misunderstood method, although many researchers purport to use it. They may employ one or two of the strategies or mistake qualitative analysis for grounded theory (Charmaz , 2017). Conversely, other researchers employ grounded theory methods in reductionist, mechanistic ways. Neither approach embodies the flexible yet systematic mode of inquiry, directed but open-ended analysis, and imaginative theorizing from empirical data that grounded theory methods can foster. Subsequently, the potential of grounded theory methods for generating middle-range theory has not been fully realised.

Until we can get big data into our Angola villages and cities, we must focus on maximizing the technology we already have. Take, for example, public transportation in Angola, which is doing tremendous work. At this point, we have tapping and going in buses, so we know who is driving and don’t have to count the number of passengers; we have video surveillance systems for CCTV to control traffic; in town, we have sensors to determine where the area is crowded; and technology is used in waste management operations in certain of our towns. These are all tools that can help to better map the city and employ technology to improve city life (Ochara .et al 2019).

Africa is expected to be the world’s fastest urbanising continent by 2035, which will have a significant influence on Angola’s growth. In addition, Cape Town collaborated with network providers to collect data from sensors strategically positioned across the city. Satellite towns would be equipped with contemporary infrastructure to attract tech-savvy enterprises. Kenya, Rwanda, Nigeria, and other countries are also working hard to build satellite towns in locations around pre-existing cities. Low carbon investments can create urban services which meet basic human needs, draw on new and increasingly affordable technologies, and generate virtuous cycles of work, mobility, energy and health (Woodgate, 2019). 

The Smart urbanisation industry has some significant challenges. The main complaint about these cities is that they try to earn the reputation of being smart by making claims about their capabilities in the commercial sector. While some cities call urban transformation projects in progress or after an initial strategy has been approved, without having implemented or produced outcomes, other cities use the word for completed initiatives. While Smart City has a great brand image and is relevant to city marketing, certain cities have also taken use of Smart City’s unique qualities for communication purposes. In order to prevent the sort of discrepancy that can arise due to the wide usage of the Smart City idea, a substantial standardisation of the classification and scope of the idea is needed (Cruz ,et al 2018 ).

Developing smart cities in Africa can solve all these concerns. Big Data analytics will help cities and administrations prepare for the future and discover the best solutions. Angola’s incredibly rich, yet tragically poor. Unlike neighbors, aid doesn’t depend on foreign help or financial organizations (Owusu ,2017). Power is centered on José Eduardo dos Santos, serving since 1979. Between 1997 and 2001, according to one review, approximately $1.7bn disappeared from the government budget. Since the Civil War in 2002, the administration has tried to increase transparency. But many donors and officials still see the country as a sea of uncontrolled corruption, says DFID poll, and experts feel Angola has not helped dispel this perception. The IMF says that Angola’s government failed to intervene to undermine the president’s power grab. It stays below the Corruption Perceptions Index. Angola joins Ibrahim 42 of 48 nations (Chiambo , et al 2019). 

Furthermore, the continent can compete in the global economy with other cities throughout the world. But, until we can get big data into our African villages, we must focus on maximizing the technology we already have. Take, for example, public transportation in Africa, which is doing tremendous work. At this point, we have tapping and going in buses, so we know who is driving and don’t have to count the number of passengers; we have video surveillance systems for  control TV to traffic; in town, we have sensors to determine where the area is crowded; and technology is used in waste management operations in certain of our towns. These are all tools that can help to better map the city and employ technology to improve city life (Ngunga , 2019

The Smart Urbanisation Problems and Covid-19 Pandemic

In another key danger, the balance between the private sector’s various interests, especially the ICT sector, and the city’s demands is noted as a significant issue. Also, decisions are sometimes made without consideration of what would be best for the city as a whole. Because of the importance placed on using technology, there is a risk of disproportionate technological advancement in Smart City, as implementing Information and Communication Technologies does not automatically make a city smart (Abusaada , 2020). 

Furthermore, this shift in the technology focus might encourage unsustainable behaviour, antiquated development models, and unproductive conduct in the market and people, since the ICT deployment is created to adapt to market and citizen behavior and not to alter it. For example, with regard to public transportation, the inequalities in the availability of infrastructural networks are a significant contributing element to socioeconomic and territorial inequality. In regard to such networks, several territories have developed their own variations in terms of accessibility, which is one of the reasons why ‘Smart Territory’ has instead of ‘Smart City’ to characterize the situation. The vast majority of so-called smart cities, according to this definition, have an underlying focus on business-led urban development in general (Kylili, et al 2020). 

When these cities see themselves as neo-liberal places where collaborative activity in the public-private sector covers a business model intended to promote companies while also eliminating the conflict-of-interest debates, they call themselves neo-liberal cities. Other elements, including education and accessibility to technology, might have an upsetting effect on the displacement of lower-income families inside the city. So, the points I would like to highlight are as follows: On the one hand, focusing on technical advancements and economic growth would lead to more social and spatial isolation, as it will lower the importance of social factors and steer the development of unsustainable cities (Sharifi , 2020).

Furthermore, there is an unchecked number of individuals who are living in extremely low conditions in Angolan cities.  Inadequate infrastructure in Angola mostly affects those in poverty.  The most significant issues that plague the populace include inadequate utility management, scarcity of water and energy, and exorbitant expenses.  Although one-third of the population of Angola has access to basic sanitation, major issues and difficulties persist in combating crime and illness in developing urban areas where poverty levels are greater. Angolan towns are faring well thanks to the worldwide epidemic (Kang , 2020). However, Covid-19 gave Angola’s cities the opportunity to adjust their thinking and the way they perform daily business. Most cities during the epidemic prioritized their problems while also challenging them. 

The crisis expedited the use of technology and computerised transactions. E-government services and online money transactions adoption has skyrocketed. Our cities are working hard to cope with the effects of the Covid-19 epidemic, and thus far they’ve been successful with their limited resources and local conditions (Kunzmann , 2020). An extraordinary demographic surge accompanied by many problems for governments, which must keep up with population expansion, housing, food, water, transit, waste management, energy supply, etc. As of now, nearly all cities are not meeting data-gathering and e-government service needs. Technology adoption in our activities is not yet perfect.  

Angola must take advantage of this increase in urban areas, of urban residents, to address these issues. In this process, technology is an essential instrument. With all these challenges Angola has the ability to solve, the development of smart cities is essential (Zhang , et al 2021)

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