Balance Sheet Of Ship Owners and Data Maturity Assignment Sample

1.0 Introduction Of Balance Sheet Of Ship Owners In The Measurement Of Data Maturity In Liner Shipping Companies

1.1 Background of the study

In these current years, the characterization of the demand volatility is observed in the understanding of the challenges like market instability & different political aggression, which have been limiting the growth rate of international trade that is being observed at 4.1%, in the year 2020. According to UNCTAD, the growth will be projected at 6.2% in the years from 2024. In this era of digitization, digital maturity would be crucial for global organizational trades & for understanding the increasing challenges for liner shipping companies (Hutter, 2019). There is also the increase in debt valuation, which will be an element of worry for maintaining the balance sheet for the owners of the shipping companies. Through the process of deleveraging, an increase in assets could be observed through which the reduction of debt could be regularized. Through the customization of the investment policies & proper utilization of the big data & CPS data & strategic alliances in the perspective of digitization to counter trade volatility, would be effective in the regularization of the balance sheet & financial operations of the International liner shipping companies.

Struggling to meet deadlines? New Assignment Help is here to rescue you! With our specialized assignment writing services in the UK, you can submit impeccable assignments on time, every time. Take advantage of our free assignment samples to understand our approach better.

1.2 Research Aim

The research aim is to conduct this research is to instigate the digital changes & to properly utilize the financial data for the effective countering of trade volatilities (Commons.wmu.se 2022). In addition, most importantly to maintain a regulated financial balance sheet through the process of deleveraging so that it can be proven helpful to reduce the debt valuation of the liner shipping organizations.

1.3 Research Objectives

The research objectives are outlined below:

• To utilize technological advancements & financial data for evaluation of the business operations.
• To create value in customer needs for cost-effective business operations.
• To instigate deleveraging measures of the balance sheet for the reduction of business debts.
• To understand the marketing opportunities of the shipping business for financial decision-making.

1.4 Research Questions

Through the analysis of the organizational objectives of the liner shipping companies, some questions will be raised for the development of the economic & financial factors for creating a balance in the debts & assets of the organization. These questions are also crucial in the business transformation & increase of revenue of the liner shipping industry.

• What measures of technological advancement & financial data will be instigated in organizational development?
• What would be the cost-effective process of business operations that creates value for the customer requirements?
• What would be effective business models & deleveraging measures for the improvement & creation of the business environment & measures?
• What would be the measures that will be instigated in gathering data to evaluate the financial decision-making of the shipping companies?

1.5 Importance of Research Question

In the assessment of the research questions, the significance of these measures will be determined in addressing the limitations of the objective fulfilment of the objectives & also could enhance in the improvement of knowledge for determining the discovery of new methods of financial development & transformation of the business structure for enhancement of financial & logistics activity (Carvalho, 2019). As this research is about the understanding of the impact of data maturity in the liner shipping business. Some different financial understandings & measures need to be evaluated in the understanding of the questions. In this making of the financial questions, the understanding of the impacts of the deleveraging process on the company balance sheet could be observed & also through the answering of the questions, future business decisions regarding business operations could be assessed (Chodorow-Reich, 2022). There is also the significance of understanding the customer requirements & to provide a valuation, which could be the benefiting factor for attracting investments & business opportunities.

1.6 Research Rationale

In the evaluation of the digital transformation & maturity, there are different measures that could be observed in the fulfilment of the goals of the liner shipping companies. There are also financial objectives of debt reduction, through increasing the valuation of the assets & revenue would be acclimatized (Côrte-Real, 2020). By highlighting the principles of the digital maturity & regulation of deleveraging of the balance sheet, the objectives could be fulfilled. Through the understanding of the activities of the liner shipping companies, there are different factors that needed to be evaluated to grow in the same frequency as the competitive business organizations. Through understanding the strategies of the successful liner shipping companies & forming economic alliances with different platforms, a variety of economic measures could be observed to the advantageous points of shipping activities. There is an economic concern about cost regulation in the business operations of the shipping companies. There are stable systems that will be benefitting cost optimization & improvement of services. By revolutionizing the digital business operations of the liner shipping companies & utilizing the financial data, the growth of the business will be effectively observed. In the instigation of the business models reshaping of the industries & businesses could be effective in the countering of business limitations. The measures of digital maturity would act as tools for the adaptation to the competitive environment & for the improvement of organizational efficiency (Felch, 2019). There are different changes in the investment policies & investment digitizations that many governments have imposed for the promotion of technological business agility & also for the global collaborative approach. There will be business collaborations that will be effective in addressing the business competitiveness & also for the maximization of the profit valuation. Some challenges could be observed in the financial operations & economic development. In this business of liner shipping, massive debt & liability valuation could be observed in the business operations & maintenance of the balance sheet of the organizations. Some provisions & regulations need to be maintained for extensive fulfilment of customer needs (Zuhroh, 2019). It is significant to understand the industry of liner shipping for the actualization of the rates of freight & shipping environment, which could be effective in countering trade volatilities & market changes, which would be effective in reducing the risk factors of liner shipping companies

2.0 Literature review

2.1 Empirical Study

A secondary research approach is used to analyze the data maturity in liner shipping businesses in the literature study by Gökalp and Martinez (2021). The capacity of an organization to effectively and efficiently handle, analyze, and use data for decision-making and performance improvement is referred to as data maturity. In order to highlight relevant topics, methodology, and conclusions, this study will present an overview of the literature on information maturity in the context of liner shipping businesses. In their first paragraph, the authors emphasize the rising significance of data-driven decision-making in the marine sector, notably in liner shipping.

They stress the need for data maturity for businesses to acquire a competitive edge, improve operational efficiency, and meet changing client demands (Gökalp and Martinez, 2021). After that, the review digs deeper into the literature, looking at several studies that looked at the maturity of data in liner shipping businesses. Common study themes are identified by the authors, including corporate culture, data governance, data quality, and data analytics. Grasp the elements that affect data maturity in liner shipping businesses requires a grasp of these topics. The authors highlight the frequency of case studies, surveys, and data analysis approaches when analyzing the methodology used in the research under consideration.

Figure 1: Chart of Earnings from Digital maturity of liner shipping industry

(Source: globalmaritimehub.com)

According to Lin et al. 2020, the secondary research techniques used to analyze data maturity in liner shipping businesses are the main subject of the literature study by the authors. To offer insights on the subject, the writers review the body of writing and research that has already been done. The importance of data maturity in the liner-shipping sector is emphasized by the writers in the opening paragraphs, along with its potential to improve operational effectiveness, decision-making, and competitiveness. They will emphasize that liner shipping businesses must achieve data maturity in order to successfully use the enormous quantity of data produced by their operations. Use secondary research techniques to do your literature reviews, such as examining published studies, academic papers, and business reports. Data management procedures, data governance, data analytics, and data-driven decision-making are just a few of the topics they research in relation to data maturity in liner shipping businesses (Greenwald, 2020). The authors pinpoint important variables affecting data maturity in liner shipping businesses through their analysis. These elements include the organizational culture, the technology infrastructure, the quality and security of the data, and the availability of qualified individuals.

Lam and Yap, 2019, focus on using secondary research techniques to examine data maturity in liner shipping businesses. The idea of data development and the consequences for liner shipping businesses in terms of efficiency in operation and competitive advantage are explored by the writers. Data maturity is described in the study's introduction as the degree of sophistication in the gathering, analyzing, and use of data inside an organization. In order for liner shipping businesses to make well-informed choices, improve operations, and successfully react to shifting market conditions, the authors contend that data competence is essential. Authors use secondary research techniques to study the subject, including literature reviews and analyses of earlier studies, reports, and trade publications (Hastig, 2020). They analyze the present level of data maturity in liner shipping businesses and pinpoint important variables including technological infrastructure, data quality, organizational culture, and data governance that affect its development. Although many liner-shipping firms have made strides in implementing cutting-edge technology and systems for data collecting, the authors note that there are still major hurdles with regard to data quality, integration, and analysis. They draw attention to the necessity of strong data governance frameworks and data-driven decision-making-friendly corporate cultures.

Figure 2: Chart of Growth prediction of liner shipping companies

(Source: www.mckinsey.com)

2.2 Theories and Models

Several theories and models may be used to evaluate and comprehend the phenomena of the deleveraging of ship owners' balance sheets in the context of secondary research methodologies on the assessment of data maturity in liner shipping businesses. Here are two notable examples:

• Theory of Financial Leverage: This theory focuses on how debt and leverage affect a company's financial performance. It implies that ship owners that depend extensively on debt financing may have difficulties when trying to deleverage their balance sheets (Teichert, 2019). High amounts of debt can lead to higher interest costs, lower cash flows, and less financial flexibility. This hypothesis may be used to study the deleveraging process by looking at the connection between debt levels, profitability, and ship owners' capacity to gradually lower their debt loads.

Figure 3: Diagram of Financial leverage trade-off Model

(Source:www.mdpi.com)

• Data Mature Model: This model focuses on evaluating an organization's data management skills and its capacity to use data to make decisions and enhance performance. The amount of data maturity inside these firms may be assessed by using this model in the context of liner shipping companies. Data governance, data quality, data analytics capabilities, and data-driven decision-making are common model components (Vásquez, 2021). The degree, to which liner-shipping firms have the infrastructure, procedures, and culture to use data efficiently in their operations, including the deleveraging of their balance sheets, may be determined by evaluating the data maturity level.

Figure 4: Diagram of data mature model

(Source: www.mdpi.com)

2.3 Literature Gap

Secondary research techniques concentrating on the deleveraging of ship owners' balance sheets and their relationship to the assessment of data integrity in liner shipping firms appear to be lacking in the literature in this area (Wagire, 2019). There is a paucity of particular studies that explore the link between these two characteristics, despite the fact that current research may touch upon areas of balance sheet management or data maturity throughout the shipping sector. Due to a research vacuum, academics and researchers have the chance to investigate the effects of balance sheet restructuring on liner shipping firms' data maturity and gain important knowledge about the financial and operational tactics used in the maritime industry.

3.0 Methodology

3.1 Research Design

In this section of the research approaches, the determination of the information will be observed in the process of research design. There is significant information that represents the effective management of the understanding of the observational factors of the industries. In this descriptive research, design which will be effective in the research situations. There is an analysis of the situation, which would be impactful in research conduction. In the qualitative approach of the research, experiences & basic conceptual beliefs will be understood for the effective findings of the solutions to the problems & for implementing new concepts & ideas. In this secondary data collection, the research measures the descriptive process & through the observation of the influential factors in the understanding of patterns, finding ideas & to actualize growth opportunities (Kamble, 2020). In this study, the analysis of customer feedback & detailed diagnosis of the industry could be the evaluating factor for the improvement & development of the services & products. This will be effective in the identification of the problems & also help in addressing the areas that needed to be improved. There is also the aspect of correlating different variances that would be effective in the data collection. Through the correlation of the variances, market trends & patterns could be understood for the business evaluation enhancement of the productivity of the fulfilment of the objectives.

3.2 Research Approach

In this section of the development of the data approach, the study will be mainly focused on deductive research approaches. Through the specific investigation of the deductive research approaches, obtaining of information from any situation would be concerning in observational premises. Through the deductive research approach, the understanding of theories & effective formulation of the secondary data. Through the utilization of the social data that are implications of the previously researched data, the association of the scientific research approach would be done. There is reasoning in the general approaches to knowledge acquisition, which would be applied in the development of theories. There is the representation of the recognition of the circumstances (Karademir, 2022). There is also an understanding of the logical reasoning of the deductive research approach. Through the specific explanation of the observations & understanding of data, patterns would be advantageous for the development of concepts & various variables. Deductive would be understood in the process of generalization of the data quality. Through the collection of the data from research information & modification of the circumstances, an understanding of logic & assumption of approaches would be considered in the assessment of the results.

3.3 Research Strategy Qualitative

In the analysis of the secondary data relating digital maturity of the liner shipping companies, the research strategy would be qualitative. Through the reported information of the qualitative research focusing on the improvement of the data collected would be effective in the questioning of the answers, which would be for the evaluation of the business activities. Through the enhancement of the research quality, the utilization of the previously researched information could be gathered at the commencement of the research. As in the significance of this research, effective answering of the questions & relative research of the pieces of evidence would be objectively controlling the trials (Kruhse-Lehtonen, 2020). There are variational changes in the contextual factors according to the research history, which will be impactful in the assessment of the leading factors & maintaining of the data quality standards. Through the information characterization & understanding of the data interpretations, several questions could be answered. There is also an understanding of the extensive plans & strategies that would be essential in the data & information transparency. In the consideration of the data insights & through the revisiting of the questions, a constructive positive approach could be observed in the policy-making & decision-making.

3.4 Sampling Method of Data Collection

In this section of the acquirement of collection of data. There are different methods of sampling that would be effective in quantitative research. Through the process of understanding the in-depth information of the researchers, the investigation of the qualitative research will be assessed (Mardani, 2020). There is the determination of the research study, which would be effective in qualifying in the determination of the subjective approaches. There is also the method of convincing sampling, through which access to the research convictions could be observed in the policy-making & effective in the organizational professionalism. Through the utilization of obtaining major research, which would be distinctive in the organizational performance evaluation. There is informational availability of the obtaining of permissions that would be a phenomenon of differential strategy making. Through the categorization of the research, interest would be practising in the innovations of the inadequate extension of the understanding of the predictive measures (Mikalef,2020). Through the method of sampling strategies, there will be the process of quality data acquirement & specification of objectives, which would have potential for the selection & achievement of objectives. In the understanding of the principles & articulation of the experiences, the importance of the probable communicative selection of experiences would be optioned for emphasis for gaining pieces of knowledge. Through these embedded strategies & comparison of the diverse valuation, an understanding of measured goals & variations of central tendencies would be effective in this theoretical approach.

3.5 Data Analysis Methods

In this portion of the effective analysis of the data, the data analysis method is observed as secondary data analysis. Through the collection & effective management of the research activities, analysis of the pre-existing data would be researched in the exploration of the research resources, which would be effective in the secondary analysis of data. However, in this research, the usage of primary data should be used in effective data analysis through the understanding of the different data management software (Munim, 2020). The Effective data segregation regarding liner-shipping organizations. There is a process of data segregation in software like Excel & SPSS that will be instigated in the countering of the issues of handling such data preparations. As there is also the secondary research approach, which would be the primary factor of the research, which will be effective in the sharing of issues results of large-scale data collection. Various agreements would be creating ethical issues in the technological advances in confidential security concerns. Some adverse concerns would be impactful in the information identification, which is available in research originality (Sanglikar, 2023). There will also be the factor of data ethics consideration of the conditional guidelines to counter the challenging factors & issues in the formulation of the data assessment.

Overwhelmed by Accounting Theories? New Assignment Help is here to make things easier for you. We specialize in providing comprehensive Accounting Assignment Help, tailored to meet your specific needs. Let our expert team handle the hard work while you enjoy the results. Contact us now for a brighter academic future!

4.0 Conclusion

In the conclusion of the proposal of the digital maturity for the liner shipping companies, various factors will be considered in the regulation of the variances. The development of the diverse approaches in the digital transformation of the shipping industry will be projected at 6.4% to 11.9 % for the enhancement of the variances. In this generation of digitization, digital maturity would be essential for the global administrative transactions & for comprehending the increasing challenges for liner shipping companies. There is also a boost in the valuation of debt, which will be an aspect of worry for preserving the balance sheet for the owners of the shipping companies. Through the function of deleveraging, an accumulation in assets could be followed through which is in the decrease of debt could be formalized. Through the customization of the acquisition policies & proper utilization of the big data, enhancement of the business in the shipping industry will be observed in the countering of competitive markets.

References

Book

• Hutter, (2019) Automated Machine Learning Available at library.oapen.org/handle/20.500.12657/23012 [Accessed on 24.6.2023]

Journal

• Carvalho, J.V., Rocha, Á., Vasconcelos, J. and Abreu, A., 2019. A health data analytics maturity model for hospitals information systems.International Journal of Information Management,46, pp.278-285.
• Chodorow-Reich, G., Darmouni, O., Luck, S. and Plosser, M., 2022. Bank liquidity provision across the firm size distribution.Journal of Financial Economics,144(3), pp.908-932.
• Côrte-Real, N., Ruivo, P. and Oliveira, T., 2020. Leveraging internet of things and big data analytics initiatives in European and American firms: Is data quality a way to extract business value?.Information & Management,57(1), p.103141.
• Felch, V., Asdecker, B. and Sucky, E., 2019. Maturity models in the age of Industry 4.0–Do the available models correspond to the needs of business practice?.
• Greenwald, D.L., Krainer, J. and Paul, P., 2020, September. The credit line channel. Federal Reserve Bank of San Francisco.
• Hastig, G.M. and Sodhi, M.S., 2020. Blockchain for supply chain traceability: Business requirements and critical success factors.Production and Operations Management,29(4), pp.935-954.
• Kamble, S.S., Gunasekaran, A., Ghadge, A. and Raut, R., 2020. A performance measurement system for industry 4.0 enabled smart manufacturing system in SMMEs-A review and empirical investigation. International journal of production economics,229, p.107853.
• Karademir, ?. and Ozgeldi, M., 2022. The Effect of Industry 4.0 Maturity on Company Performance in Manufacturing Companies.International Journal of Scientific Research and Management,10(04).
• Kruhse-Lehtonen, U. and Hofmann, D., 2020. How to define and execute your data and AI strategy.Harvard Data Science Review,2(3), pp.1-9.
• Mardani, A., Kannan, D., Hooker, R.E., Ozkul, S., Alrasheedi, M. and Tirkolaee, E.B., 2020. Evaluation of green and sustainable supply chain management using structural equation modelling: A systematic review of the state of the art literature and recommendations for future research.Journal of cleaner production,249, p.119383.
• Mikalef, P. and Krogstie, J., 2020. Examining the interplay between big data analytics and contextual factors in driving process innovation capabilities.European Journal of Information Systems,29(3), pp.260-287.
• Munim, Z.H., Dushenko, M., Jimenez, V.J., Shakil, M.H. and Imset, M., 2020. Big data and artificial intelligence in the maritime industry: a bibliometric review and future research directions.Maritime Policy & Management,47(5), pp.577-597.
• Sanglikar, K.U.N., 2023. An Effect of Cognitive Ability Emotional Maturity and Creativity on Academic Achievement among Secondary School Students.
• Teichert, R., 2019. Digital transformation maturity: A systematic review of the literature.Acta universitatis agriculturae et silviculturae mendelianae brunensis.
• Vásquez, J., Aguirre, S., Puertas, E., Bruno, G., Priarone, P.C. and Settineri, L., 2021. A sustainability maturity model for micro, small and medium-sized enterprises (MSMEs) based on a data analytics evaluation approach.Journal of Cleaner Production,311, p.127692.
• Zuhroh, I., 2019. The effects of liquidity, firm size, and profitability on the firm value with mediating leverage.

Article

• Wagire, (2019) Development of maturity model for assessing the implementation of Industry 4.0: learning from theory and practice Available at www.tandfonline.com/doi/abs/10.1080/09537287.2020.1744763 [Accessed on 24.6.2023]

Website

• Commons.wmu.se (2022) Measurement of Digital Maturity in Liner Shipping Companies' Business Models Available at commons.wmu.se/cgi/viewcontent.cgi?article=3100&context=all_dissertations [Accessed on 24.6.2023]