The Benefits of Data Strategic Applications in the Aviation Industry

Project Summary

This thesis aims to investigate the benefits of data strategic applications in the aviation industry. The study seeks to examine the impact of data strategic application and digitalization on the curated travel experience, security identification, customer experience, and airport services. Data strategic applications, digitalization, and other improvements constitute an extension framework beneficial for any market, particularly in the aviation industry (Hannigan, Hamilton & Mudambi 2015).

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Data strategic applications include artificial intelligence (AI), big data (BD), Internet of Things (IoT), Blockchain innovation, and 3-D printing. This significance arises primarily in the fast-evolving character of the market and components like the cost differentiation, customer safety, travel experience, and business competition. Hannigan, Hamilton, and Mudambi (2015) argued that many facets of airport operations, for example, security facilities, services provided, and employees affect passenger perceptions of service quality. The capacity to manage security identification, control customer expectations, and improve the travel experience enhances the competitive standing of an airline operator.

However, data strategic applications have significant challenges because of the ineffectiveness of approaches implemented and communication techniques used by airline management (Jeeradist, Thawesaengskulthai & Sangsuwan 2016). Customer experience and interaction with security and travel experience, services, and technology are challenges facing the airline industry (Jeeradist, Thawesaengskulthai & Sangsuwan 2016).

Data strategic applications have been characterized in several similar manners because of their developing character. Data strategic application is a framework that integrates data devices and web-based technologies like big data and artificial intelligence to enhance information excellence, solution leadership, collective intimacy, and speed up innovation (Martin-Domingo & Martín 2016). Digital transformation and other important financial evolutions are motivating airlines to boost their performance. Since data strategic applications in the airline industry can improve effectiveness, value, and digitalization, this research will examine the variables that show positive integration.

These variables include pricing structure, security identification, travel experience, and the competitive intensity of the industry. These variables differentiate the aviation sector business from other developing business and determine its sustainability development.

Data strategic implementation is shaping investments based on similar trends that permanently have altered economic growth (Skorupski & Uchroński 2016). The exponentially growing phase attracts not only new challenges but poses various prospective benefits for business and investors acknowledging its scope. The benefits include functional agility, service differentiation, brand integrity, increased earnings, and cost reductions.

Although business competition is growing, there is a requirement to understand clients and react to their unique needs and desires (Skorupski & Uchroński 2016). Thus, investors try to maximize profits by increasing their wallet share of passengers and reduce the cost of operations by focusing on data application technology and its implementation across various departments. With these constraints, challenges, benefits, and limitations, this study will investigate the benefits of data strategic applications as it affects curated travel experience, security identification, and other service implications. The study will conduct a structured literature review using available databases.

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The research will use a descriptive research technique to broaden data collection. Under the descriptive approach, the researcher will adopt qualitative and quantitative designs to extract information concerning the customer experience, technology patterns, and travel experience.

Topic Literature Review

Data strategic application and digitalization support revenue management. Revenue management is the act of implementing dynamic pricing to maximize sales (Skorupski & Uchroński 2016). It is difficult to measure service quality because clients have different perceptions of service value and have optimum cost factors for flight tickets. Flight cancelations and delays affect the airline sector. These challenges influence business sustainability and competitive advantage.

Therefore, many operators have installed detectors to gather information about the technical requirements of each aircraft. The advancement in predictive maintenance enables operators to forecast when a specialized error will appear and intervene to repair it. Predictive maintenance decreases operating costs and enhance business development. Information analytics increase productivity by creating key performance indicators that assist employees in remaining dynamic and fulfilling operational objectives. Digitalization is a significant source of competitive edge. Information analytics could be implemented for different reasons.

Information analytics can be used to forecast client behavior, procedures, or handling risk. It provides comprehensive and predictive insights to enhance organizational operations. Since the airline market is extremely cost-sensitive, rivalry in the business is fierce. Data strategic applications enhance critical competitive benefits for investors.

Milkau and Bott (2015) defined data strategic applications as integrating complex systems, internet-based technology, and information analytics in the communication process between customers and market representatives. These market agents include passengers, investors, equipment suppliers, stakeholders, and manufacturers. This definition is suitable for considering the aim of this research.

Data strategic implementation supports digital approaches that provide direction, create initiatives, assess performance, quantify their growth, and redirect observational goals. The authors suggested that airline operators could implement data strategic application to improve the client-based design or a digitized architecture. The client-based model allows operators to change their services to suit new customer requirements and to create curated experiences that improve consumer loyalty. Digitized architecture creates new price regimes for clients using different innovative designs (Ross, Beath & Sebastian 2017).

Upadhya (2016) emphasized that electronic communication programs have been harmonized with functional ‘beacons’ accessible from the airport, which enable location identification by customers. Such ‘beacons’ enhances customer experience during preflight and post-flight services. The technology provides airport directions to avoid flight delays (Upadhya 2016). The beacon-based location program reduces the time-wasting procedures at security checkpoints (Straker & Wrigley 2016).

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Some airports, such as Dubai International, have embraced facial recognition technology that minimizes the need for clients to present boarding passes after the check-in session. Data strategic applications can be used to control runway lighting equipment, which is a key element in maintaining aircraft balance and identifying security threats. Maintaining a safe and secure aircraft movement is the basis of efficient and stable operations to guarantee client safety, security identification, curated travel experience, and sustainable business operations. Although some airports use legacy programs, digital data strategy enhances service availability, employee performance, and improves safety.

Pigni and Piccoli (2016) opposed the use of fiber optics to control the runways and border lighting. The authors suggested that overreliance on fiber optic cables is insecure because they are vulnerable to attacks during airport functions and could be sabotaged by terrorists (Straker & Wrigley 2016). Any interference or sabotage with the functioning of this fiber optics will affect communication between flight controllers and pilots, and this act has a devastating outcome. It emphasizes the requirement for airport operators to embrace data strategic applications that use cloud solutions as a backup technique. Using these apparatus lowers the expenses of predictive maintenance and mitigates vulnerability to internal and external threats (Straker & Wrigley 2016).

Big Data in Aviation

The aviation business is undergoing extreme expansion, which will force airline operators to search for the best resources to function as an ever-expanding sector (Vranken 2017). Some experts are considering altering their business models to gain a competitive edge. The aviation sector creates and manages customer information using digitized architecture. Many airlines cannot control and process the number of information they collect from passengers and stakeholders. Based on this assumption, Big Data has become an expansion tool that connects and analyzes data inputs to improve service delivery.

This technology has created the notion of a service-oriented model that supports business development (Vranken 2017). Data obtained from the engine display unit enables effective predictive maintenance. The fuel tracking system could be analyzed to create efficient and effective fueling choices. Weather information may be utilized to ascertain how different flying conditions affect engine operation and pick the most effective mission route. Unit monitoring data may be used to determine upgrade priorities. These operations and data measurements support revenue management, curated travel experience, and service efficiency.

Effect of Data Strategic Applications and Digitization

Schwartz (2014) asserts that digitalization improves flight operations, reduces flight cancellation, and delays. Digitalization promotes efficiency by encouraging the implementation of system sequences between flight controllers and pilots. Scherer, Wünderlich, and von Wangenheim (2015) believed that data strategic applications encourage the adoption of a new reconnaissance technology. The implementation strategy supports an efficient air transport system. The new platform will replace the present sensor systems using a satellite-based infrastructure that can guarantee security identification with precision.

Lee and Park (2016) believe that digitization and data strategic applications play a significant role in sustaining small carrier investments. The low return on investments is a major challenge for the same carrier airports. However, the availability of data strategic applications eases remote management of these airports with its controls positioned in bigger airports. Phillips (2016) suggested the possibility of air traffic personnel using data strategic applications to fly and land aircraft in smaller airports. Such prospects translate into high levels of consumer fulfillment because of service expansion along local routes.

The possibility to manage different flight towers using data strategic applications illustrates the significance of digital technologies in improving the consumer travel experience and communication channels. Padrón et al. (2016) stated that digitalization provides backup to aid contingency planning during sabotage or emergencies. Data strategic applications in aviation improve service efficiency and reliability via the optimum utilization of data infrastructures.

Objectives of the Study

The goal of this research is to assess the significance of data strategic applications and digitization on the curated travel experience, predictive maintenance, customer service models, security identification, and revenue management. The research will investigate the impact of digitization strategy and its challenges in the aviation industry.

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  1. Identify the impact of data strategic application and digitalization in the airline industry.
  2. Determine the relationship between data strategic application and digitalization.
  3. Determine the significance of digitalization on the curated travel experience and customer satisfaction.

Research Questions

Recognizing the research scope, this thesis plans to concentrate the analysis on the benefits of strategic data applications in the airline sector. Based on the research objectives, the study will answer the undersigned questions.

  1. What is the impact of data strategic application and digitalization in the aviation market?
  2. What is the relationship between data strategic applications and digitization?
  3. Is there any relationship between digitization and curated travel experience?

Project Outcome

The research findings will be beneficial to airline operators, investors, policymakers, stakeholders, researchers, and customers. For airline operators and investors, the analysis will provide valuable information about data strategic implementation.

Most airline operators can use this analysis to guide the decision-making process. The recommendations of the analysis will guide policymakers in drafting new regulations and standards for data deployment in the aviation industry. Airline customers will understand the importance of data strategic applications as it concerns their safety, travel experience, and service satisfaction. The findings of this study will be used as a springboard for future investigations in the challenges of data strategic applications. This understanding will guide researchers towards new assumptions and the hypothesis for cross-examination.

Motivation for the Project

Airline operators gather a large amount of information daily. It accounts for the industry’s success in creating customer loyalty flyers and starting several database programs, which enable flight business to maximize ticket sales. With digital expansion, airline operators gather much more information, which may be leveraged via extensive information analytics to achieve competitive benefits.

Even smaller investments enjoy data strategic applications and data analytics by prioritizing data asset requirements for content analysis. Airline experts categorize data strategic applications into four areas, which include revenue management, effective personalization, consumer-centric business model, predictive maintenance, and operational efficiency. Given these indicators, the motivation to examine the impact of data strategic applications on these elements is consequential. A different source of inspiration that leads to a more profound issue towards this subject has been the alarming number of airlines that do not have an active data strategy.

Most works of literature have suggested that data strategic implementation is a gradual process. However, the lack of concern about data strategic application and digitalization stimulates the motivation for this investigation.

Research Method

A study design is a strategy used to integrate different areas of the research and attain the desired objectives. A research design facilitates the effective utilization of data collection techniques to answer specific questions in clear terms. Based on the research objectives, this researcher will apply several research methods to collect data and answer the research questions. The researcher will conduct a structured literature review and a descriptive research design.

Under the structured literature review, this study will explore different works of literature and factors similar to this research to match the assumptions and definitions that have been examined. This technique involves gathering existing information from several databases, such as research papers, articles, aviation publications, and digital sources. The information gathered from massive databases will depend on its significance and reliability. The research used specific keywords for data extraction. The Google search engine was used to find key strings like the Internet of things (IoT), Big data, artificial intelligence, Blockchain technology, and data strategic applications.

Given the comparative ascendancy of data strategic applications and limitations access to investigate its use, the descriptive technique is most appropriate for this study. The descriptive research design promotes the adoption of qualitative and qualitative approaches for data collection and analysis (Noble & Smith 2015). Descriptive research layout is an appropriate technique because it supports the analysis of curated travel experience, passenger perceptions, security identification, and service quality. A descriptive research strategy shows some qualitative features of the passenger that cannot be measured using deductive techniques. This research design allowed the researcher to guarantee the confidentiality and reliability of the study procedure.

Research philosophy is a conviction about the methods of data collection. Positivism asserts that the techniques and methods of sciences are important in social research and can be deployed to examine social phenomena. However, interpretivism states that technologies and approaches of sciences do not apply to social phenomena. This study will adopt a post-positivism strategy, where it recognizes the independent knowledge of the researcher in distributing and evaluating the findings of the investigation. The researcher will use a structured literature review to examine the impact of data strategic applications in the aviation industry.

A descriptive strategy will be used to test the predetermined theory to determine causality, utilizing empirical approaches. The inductive strategy aims at assessing the sample to create new concepts and answer the study questions linked to the research objectives. The analysis will adopt probability sampling to gather data from participants. Probability sampling is effective because it offers independent selection patterns among distinct classes.

Therefore, the system provides an equivalent probability and contains less prejudice in sample choice. Such a strategy facilitates high data reliability and validity. Noble and Smith (2015) emphasized that a precise and transparent outline of the study procedure preserves consistency and neutrality. To guarantee the trustworthiness of this information, the researcher sustained a decision path to guarantee that the conclusions were transparent and flawless.

The observations will provide the opportunity to understand how clients assess information during active flight schedules. Using direct observation, the researcher will find out whether clients could locate the beacon signals using their smart devices to avoid delays. The questionnaires will be administered as the second phase of the sampling process.

Among the features of utilizing the survey strategy is that it allows the grouping of considerable amounts of information from a small sample population at a reduced cost. It is easy to measure and analyze the information collected using nominal resources. Nevertheless, questionnaires may not provide reliable information as it relates to passenger perceptions of curated travel experience. The structured literature reviews will be used to balance the research information deficiency.

Yin (2014) said a quantitative study contains information evaluation through the arrangement and organization of information converted to codes and subjects. Based on these research assumptions, several statistical tools were used to test the reliability of the observational outcome. The data analysis process will include information coding, data cleansing, record matching, sample proofing, and evaluation.

Primary and Secondary Data Sources

The research will use reliable primary and secondary data sources. Credibility is attained by showing that the outcomes are acceptable from the participants’ view. Transferability occurs when the consequences of this study can move to other circumstances and prospective research. Dependability means the test data is reliable. Dependability addresses modifications and changes that alter the researcher’s investigation (Yin 2014). Based on these assumptions, the articles used in this investigation will be recent and reliable. The researcher will review materials and journals published between 2014 and 2019. The secondary data will include articles, journals, and online sources. The selected airline employees and passengers that use the carrier will form the primary data for this research.

Draft Chapter Heading the Report

Acknowledgment

Abstract

  • Table of Content
  • List of Table
  • List of Figures
  • List of Abbreviations
  1. Introduction.
  2. Objectives.
  3. Research Methodology.
  4. Literature Review.
  5. Methodology.
  6. Case Study, Data Collection, Survey & Analysis.
  7. Results.
  8. Discussion.
  9. Conclusion.
  10. Recommendation.
  11. References.
  12. Appendix.

Table 1. Work Plan.

Tasks Start date End Date Duration ( Days)
1. Introduction 30/09/2019 06/10/2019 7
2. Objectives 07/10/2019 12/10/2019 5
3. Research Methodology 13/10/2019 19/10/2019 6
4. Literature Review 17/10/2019 15/10/2019 5
5. Methodology 16/10/2019 23/10/2019 7
6. Case Study, Data Collection, Survey & Analysis 24/10/2019 08/10/2019 5
7. Results 09/10/2019 16/10/2019 7
8. Discussion 17/10/2019 01/11/2019 5
9. Conclusion 02/11/2019 02/11/2019 1
10. Recommendation 03/11/2019 08/11/2019 5
11. References 09/11/2019 14/11/2019 5
12 Appendix 15/11/2019 20/11/2019 5
1st Draft 21/11/2019 31/11/2019 10
2nd Draft 01/12/2019 11/12/2019 10
Work Plan.
Figure 1. Work Plan.

Reference List

Hannigan, T, Hamilton, R & Mudambi, R 2015, ‘Competition and competitiveness in the US airline industry’, Competitiveness Review, vol. 25, no. 1, pp. 134-155.

Jeeradist, T, Thawesaengskulthai, N & Sangsuwan, T 2016, ‘Using TRIZ to enhance passengers’ perceptions of an airline’s image through service quality and safety’, Journal of Air Transport Management, vol. 53, no. 3, pp. 131-139.

Lee, Y & Park, J 2016, ‘Impact of a sustainable brand on improving business performance of airport enterprises: the case of Incheon international airport’, Journal of Air Transport Management, vol. 53, no. 1, pp. 46-53.

Martin-Domingo, L & Martín, J 2016, ‘Airport mobile internet and innovation’, Journal of Air Transport Management, vol. 55, no. 1, pp. 102-112.

Milkau, U & Bott, J 2015, ‘Digitalisation in payments: from interoperability to centralized models? Journal of Payments Strategy & Systems, vol. 9, no. 3, pp. 321-340.

Noble, H & Smith, J 2015, ‘Issues of validity and reliability in qualitative research’, Evidence Based Nursing, vol. 18, no. 2, pp. 34–35.

Padrón, S, Guimarans, D, Ramos, J & Fitouri-Trabelsi, S 2016, ‘A bi-objective approach for scheduling ground-handling vehicles in airports’, Computers and Operations Research, vol. 71, no. 1, pp. 34-53.

Phillips, S 2016, ‘New technologies simplify the airport experience. Airport Business, vol. 30, no. 5, pp. 18-22.

Pigni, F & Piccoli, G 2016, ‘Digital data streams: creating value from the real-time flow of big data’, California Management Review, vol. 58, no. 3, pp. 5-25.

Ross, J, Beath, C & Sebastian, I 2017, ‘How to develop a great digital strategy’, MIT Sloan Management Review, vol. 58, no. 2, pp. 7-9.

Scherer, A, Wünderlich, N & von Wangenheim, F 2015, ‘The value of self-service: long-term effects of technology-based self-service usage on customer retention. MIS Quarterly, vol. 39, no. 1, pp. 177-200.

Schwartz, V 2014, ‘Dimanche a Orly: the Jet-age airport and the spectacle of technology between sky and earth’, French Politics, Culture and Society, vol. 32, no. 3, pp. 24-44.

Skorupski, J & Uchroński, P 2016, ‘Managing the process of passenger security control at an airport using the fuzzy inference system’, Expert Systems with Applications, vol. 54, no. 3, pp. 284-293.

Straker, K & Wrigley, C 2016, ‘Translating emotional insights into digital channel designs’, Journal of Hospitality & Tourism Technology, vol. 7, no. 2, pp. 135-157.

Upadhya, V 2016, ‘Airport transformation through digital passenger experience. Airport Business, vol. 30, no. 4, pp. 38-41.

Vranken, H 2017, ‘Sustainability of bitcoin and blockchains’, Current Opinion in Environmental Sustainability, vol. 28, no. 1, pp. 1-9.

Yin, R 2014, Case study research: design and methods, 5th edn, Sage Publications, Thousand Oaks, CA.

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StudyCorgi. (2021, August 3). The Benefits of Data Strategic Applications in the Aviation Industry. Retrieved from https://studycorgi.com/the-benefits-of-data-strategic-applications-in-the-aviation-industry/

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StudyCorgi. "The Benefits of Data Strategic Applications in the Aviation Industry." August 3, 2021. https://studycorgi.com/the-benefits-of-data-strategic-applications-in-the-aviation-industry/.

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StudyCorgi. (2021) 'The Benefits of Data Strategic Applications in the Aviation Industry'. 3 August.

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