Inter-organizational Innovation Networks (IT): Role of cloud computing and big data on Business Model Innovation
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Abstract
This report looks at how cloud computing and big data impact business model innovation and if these technologies enable companies to implement quicker and more efficient means to interact with customers and enhance their profitability and growth potential. Meta-analysis was conducted by accessing peer-reviewed literature which targets issues surrounding the research problem and research questions. Despite the uncertainty in terms of knowledge and expertise in the IT industry due to rapid technological developments there are still opportunities to be leveraged by businesses. However, in answer to the research questions it is found that cloud computing and big data does attract and provide value to customers because they can access larger volumes of data in real-time. Moreover, it is evident in this report that these technologies do positively impact business management strategies as they can reduce operating and data storage costs and also allow more effectively communication with customers. However, there is uncertainty posed by the challenges found with smaller companies which lack sufficient resources to fully exploit cloud-based technologies. This report recommends that empirical qualitative research should be conducted to ascertain personal perspectives from personnel located within the practical environment which may better inform the research problem, answer the research questions and better achieve the research objectives in terms of enhancing the strengths and reducing the weaknesses noted in this report.
Chapter 1: Introduction
1.1 Research Problem
1.2 Research Questions
1.2.1 How does cloud computing and big data attract and provide value to customers?
1.2.2 How does cloud computing and big data impact business management strategies?
1.2.3 How does cloud computing and big data contribute to corporate profitability and growth?
1.3 Statement of Research Objectives
This report is designed to address the research problem, answer the research questions and also conduct a critical analysis of how this research is conducted, of the processes used to research this project and what findings were obtained. Furthermore, the strengths and weaknesses of the research is reported as is specific qualities of the research and possibilities of forming a conclusion from the results. Moreover, theoretical proposals are submitted which can be translated to inform and influence practice within the corporate setting.
1.4 Background
This report essentially investigates how the innovative business model can be impacted by the introduction of cloud computing and big data so that corporate management can increase their sales, provide a more cost effective marketing program and also achieve additional profits and long term growth. Teece (2010) suggests that an innovative business model can be viewed as a concept which targets specific technologies and innovative features as a way to provide enhanced value and benefit to the customer. In order to design this type of customer-orientated business model the company should identify specific markets or niches which can benefit from the introduction of innovative technology and big data.
Therefore, potential revenue streams will need to be identified so that the company can also benefit from supplying to market demand. This will require that specific mechanisms are found which can capture different products and services which fill specific needs which are not only evident currently but also in the longer term. This ensures that the company is set to maintain a sustainable route to profitability and growth thereby rewarding its shareholders and benefiting all its stakeholders. This means that the applications of cloud computing and big data can be leveraged to address what customers want, the manner in which products and services are delivered, how the company organizes its infrastructure to address these demands and also offer increased potential possibilities for sustainable additional profits and growth (Teece, 2010).
Cloud computing can allow a company to reduce the costs of internal data storage by transferring data from internal fixed and variable cost mechanisms into new innovative data storage models which are efficient and can be quickly accessed not only by the company but also by its stakeholders, including its customers, employees, suppliers and other parties connected with the company’s operational functions. Big data is defined as the capacity to analyze all transactions and obtain insights from all the customers’ interactions both quickly and efficiently (Bughin, Chui & Manyika, 2010). This capacity is generated via technologically enhanced access to customer related data from “public, proprietary, and purchased sources, as well as new information gathered from Web communities and newly deployed smart assets” (p. 7). In reality, big data can enable the transformation in how innovation, marketing and research are conducted within the corporate environment.
1.5 Research Outline
Chapter 2 explores existing peer reviewed literature so that different views and contrasting opinions can be gained so as to further inform the study. This meta-analytical approach has the capacity to capture various sources of data pertaining to the research problem and the research questions and use to this data to determine validated findings. Chapter 3 looks at the research methodology and research methods which are utilized to identify and collect the data. Chapter 4 is constructed to ascertain an analysis and findings pertaining to the data collected in the literature review; such is designed to review how cloud computing and big data impacts the corporate environment both in terms of theoretical concepts and also how these concepts can be translated and applied to the practical setting and perform an intervention role. Chapter 5 offers conclusions and recommendations which target the research questions and objectives as outlined in Chapter 1. This is enabled by offering potential contributions and solutions found in the literature review, evaluating the analysis and findings and also perform a reflective stance in how the findings can impact future research coupled to the inclusion of recommendations for both the corporate setting and also research in general.
Chapter 2: Literature Review
Research maintains that while business models offer choices which create the company’s architecture framework innovative technology possesses the ability to expand the framework to generate profits, growth and value (Teece, 2010). Technological innovation can leverage competitive advantage within business model innovation via reduced operating costs and increased value to the customer. Cloud computing can be formatted to provide security and protection to the corporate user via encryption technology so that their competitive advantage is not compromised by imitation or access to intellectual property. Teece (2010) suggests that to possess competitive advantage, business model innovation should be capacitated to not only conduct a logical method of systemized operations it should also be designed to target specific consumer requirements. Cloud computing offers the consumer the option of real-time data from almost any location which meets their immediate requirements (Teece, 2010).
Cloud computing and big data possesses the collaborative ability to construct both online corporate and consumer communities to develop, sell and support both products and services from the supplier to the point of demand. This can allow companies to exploit the opportunities offered by social media and networking as a means to communicate with both existing and potential customers. Furthermore, it also has the ability to open up corporate boundaries thereby allowing external resources and talent to offer innovative expertise (Bahrami & Singhal, 2015). This means that both internal and external resources are leveraged by the flexibility offered by cloud computing and big data. Another ability offered by such technologies pertains to the increase of those who do not perform production or transactional roles but rather possess an expertise in the acquisition of knowledge. According to Bughin et al. (2010) knowledge personnel are higher paid than production workers so these technologies possess the capacity to maximize the acquisition of knowledge and data via sharing and collaboration. The researchers further noted that these technologies provided what they defined as the “Internet of Things” (p. 6) or “smart assets” (p. 6). They suggested that this phenomenon was embedded with actuators, communications capabilities and sensors which have the capacity to transmit data on an enormous scale and can also automatically adapt to react to changes and volatility found within the corporate setting.
Cloud computing and big data allows a company to customize, measure, monitor, and allocate the utilization of assets via outsourcing or accessing business-to-business (B2B) customers; such enables a company to avoid unnecessary capital investments but rather access external resources and expertise (Zhang, Cheng & Boutaba, 2010). The inclusion of these technologies into multi-sided form of business model innovation can include multiple participants rather than the more traditional one-on-one exchange of data and information. Income streams such as advertising can generate content to corporate audiences while gaining revenue from external or outsourced third parties. Moreover, income via customer subscriptions can be facilitated quickly and efficiently via cloud computing and access to big data (Hashem, Yaqoob, Anuar, Mokhtar, Gani & Khan, 2015). These technologies further develop efficiencies within the corporate supply chain by removing the necessity to store huge amounts of data and inventory but rather to supply demand as and when it occurs. The combination of these technologies to business model innovation can allow greater connectivity between the suppler and the consumer such as sensors which transmit real-time data between supply and demand points (Assunção, Calheiros, Bianchi, Netto & Buyya, 2015).
These technologies have also allowed search engines such as Google, Microsoft and Yahoo to create new business model innovation via the capture of data which is freely available online and offer it to both the public and businesses in formats and applications which are useful and user-friendly; such offerings can be provided in the form of satellite images, transportation directions, image retrieval and satellite imagery and transform how people can access data and resolve immediate problems (Bryant, Katz & Lazowska, 2008). According to other research, big data computing via the cloud possesses the potential to collect, organize, and process all forms of corporate data so as to reduce costs and enable user convenience (Low, Chen & Wu, 2011).
According to Demirkan and Delen (2013) a challenge posed within a fast changing business environment pertains to the demand for the capture of consumers’ rapidly changing requirements and also their expectations. In order to cope with these challenges companies will need to adapt their strategic decision-making capabilities via service-oriented decision support systems (DSS) so that cloud computing and big data technologies can be applied so that instead of allocating scarce resources the company can leverage rentals rather than purchasing assets. Furthermore, this can allow the creation of service level alliances rather than the acquisition of product supply contracts thereby enabling a virtual outsourcing of infrastructural support systems and contractual obligations.
Brown, Chui and Manyika (2011) noted that competitors were investing more into the capacity to collect, integrate, and analyze data not only from a centralized head office but also throughout each point in the supply chain. Using the earlier noted technologies competitors can conduct almost countless real-world testing and experimentation of big data thereby allow the automated adjustment of prices in real time so as to immediately inform supply points and enable just-in-time (JIT) distribution. The constant “testing, bundling, synthesizing” (p. 1) enabled competitors an advantage by the agile movement of instant data throughout the company infrastructure. This movement allows the monitoring of employee functions, track purchase orders and determines consumer buying patterns and behavior.
Low, Chen and Wu (2011) maintain that the adoption of cloud computing and big data does not offer equal opportunities for corporate organizations and that there are possible pitfalls or issues which can serve to deter its potential usefulness. Overloading of demand to the provider can lead to periods of downtime and power outages in addition to other periods which are set aside for the purposes of provider maintenance. Another challenge to the adoption of these technologies concerns the increased complexity in the development of these technologies which means that companies possessing a high level of IT expertise may be better positioned to leverage such technological benefits while other companies without the internal IT resources and expertise can be disadvantaged.
According to Agrawal, Das and El Abbadi (2011) while these technologies offer significant operational and strategic opportunities dependency by some companies on external resources and expertise as a means to leverage these technologies can serve to deter the potential exploitation of these technologies. The high-tech industry were found to be the most likely to adopt these technologies as they could benefit from offering cloud computing services to companies with less IT-orientated resources; thereby the concept of both cloud computing and big data was not complete as it did not present equal benefits to companies.
However, Low et al. (2011) argued that companies without the necessary IT knowledge can implement a gradual phasing in of these technologies and can implement learning and training programs designed to fully arm companies with the necessary know-how. Agrawal et al. (2011) noted that in order for companies to become independent of the external expertise and IT resources needed to efficiently adopt these technologies better internet infrastructure and mobile electronic equipment was needed. In addition, due to the rapid advancement in these technologies companies will be required to invest in ongoing IT expertise either internally or externally in order to leverage the benefits afforded by these technologies. Low et al. (2011) contended that both software based enterprise resource planning (ERP) and customer resource management (CRM) were systemized so that even companies possessing lesser IT skills and knowledge can exploit the benefits of these technologies. They further contended that such systemized software applications offered significant advantages to companies in terms of competitive advantage while requiring relatively little IT expertise and knowledge. However, both Agrawal et al. (2011) and Low et al. (2011) agreed that currently (2011) the provision of cloud computing was biased towards the external provision of services and therefore dependency of companies on external providers rather than companies achieving independence via the acquisition of sufficient expertise and knowledge. Low et al. (2011) further claimed that the capacity to exploit these technologies was dependent on variables such as “relative advantage, firm size, top management support, competitive pressure, and trading partner pressure” (p. 1020). This suggests that currently the full benefits of cloud computing and big data were more likely to be adopted by larger resource-rich companies. Low et al. (2011) also claimed that peer pressure or trading partner pressure was a factor which drove the acquisition of these technologies yet the “charging mechanisms” (p. 1020) of cloud computing services were not perceived within the industry as friendly towards the adoption of these technology as they relied on dependence on external resources and expertise.
Chapter 3: Methodology
3.1 Research Approach
According to Leedy and Ormrod (2005) both qualitative and quantitative research (mixed methods) can be used to investigate existing peer reviewed literature resources. This allows the examination of quantitative data found in objective statistical or numerical findings to be compared to a more subjective stance in which findings were obtained direct from personal experiences found in the practical setting; such by the meta-analysis of different researchers’ findings. As noted earlier in the Literature Review, Low et al. (2011) and Agrawal et al. (2011) disagreed on various issues surrounding the applications of these technologies based on their qualitative and quantitative findings therefore this report concedes that there are limitations posed by the lack of sources and evidence relating to which of these researchers’ views are more credible or valid although the other meta-analysis were largely supportive of the stance adopted by Low et al. (2011).
3.2 Data Collection
The data was collected via entering key search words into search engines such as Google Scholar such as big data and cloud computing however on reflection other key search words such as ERP and CRM could have been used to evaluate a more systemized software-based approach in which smaller companies may have been better positioned to exploit these technologies.
Chapter 4: Results & Analysis
Based on the evidence offered in both Chapters 1 and 2 it is apparent that both cloud computing and big data offers significant advantages to companies in the form of cost savings. These savings are in terms of alternatives to the traditional necessity for companies to incorporate internalized large database storage facilities as they grow and expand. The transfer of storage to external cloud computing providers can allow the company’s human and logistical resources to be more effectively allocated into profit related strategies such as online marketing and exploitation of social media as a means to reach customers. However, the literature review also cautioned that the adaption of cloud computing and big data was challenged by unforeseen issues such as interruptions in communications as well as the inability of companies to adapt to these technologies due to the bias by providers to force companies to be dependent on external expertise and knowledge. Moreover, evidence infers that factors such as the company’s size can be a determinant of its ability to exploit these resources suggesting that larger companies may possess an unfair advantage over their smaller rivals.
Chapter 5: Conclusions and Recommendations
In conclusion, cloud computing and big data plays a significant role in the performance of business model innovation as these technologies serve to allow companies to offer quicker and more efficient communication mechanisms to their customers thereby enhancing consumer experience; such essentially provides increased real-time value and convenience to the customer. This report has found that because these technologies are still in the process of rapid development the opportunities afforded to businesses are perhaps more biased towards larger companies due to the high levels of expertise and knowledge which are needed to optimize these technologies. Therefore, cloud computing and big data impact business management strategies differently depending on their resources and size.
However, cloud computing and big data possess the potential contribute to corporate profitability and growth as they not only allow larger amounts of data to become available but can also reduce the company’s data storage costs via the leverage of external could computing resources. In terms of strengths and weaknesses, this report considers that the accessed data has offered additional insight in terms of the research problem yet falls short of fully answering the research questions. This is because the analysis and findings clearly point to the advantages posed by these technologies yet fails to fully address how these technologies can benefit smaller companies which may not possess sufficient IT knowledge and expertise.
While these findings may offer a contribution to researcher and provide a basis for future researchers to further investigate the research problem it is also recommended that maybe more qualitative empirical research should be conducted by future researchers so that more current insight may be gained from the practical corporate setting; such can therefore allow theoretical concepts to be translated into meaningful practical applications and interventions. It is recommended that companies in general should be encouraged to participate in interviews or survey questionnaires so that more practical interventions can be brought to bear on the research problem.
References
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