Leveraging Technology in the Tourism Industry

Abstract

Study focuses on tourism industry with a specific organization in choice that enables people to gain a travel service in excellent way. It uses certain technologies to become successful in business. Artificial intelligence is always enhancing business operators and differentiation of networking technologies is going to be highlighted here. All travel-related concerns are covered in the system of GoIbibo. It also allows the web check-in features which is to select the seats as a person moves forward. They also provide data safety which is important for the organization to gain customer trust and loyalty. An analysis is presented that will reflect on the advantage and disadvantage of this organization’s use of IT. For students seeking expertise in IT dissertation help, understanding how such technologies integrate into the travel industry can be crucial.

Introduction

This study is going to highlight the different types of application software that can be used in business. In order to run a business using different types of application software has become important. In today’s world data security is being largely impacted since people are keen on their data safety and keeping their privacy intact. This study is divided into various stages and it will give information on ethical and social issues that are increasingly becoming important in the world. Discussion of business issues in order to find out data safety is important. In a selected business sector there are certain pain points that need to be covered up. The study shows a concept of selecting a business and managing the process that helps in managing information technology.

Literature review

Different types of application software

Application software is associated with the tourism industry which is GoIbibo. It is one of the renowned organizations that gains attention by serving customers with their best ever services and IT components. “Goibibo is a leading travel aggregator whose business transactions are performed online” (Bisong, 2019). The mode of business in the case of Ibibo is B2C and customer aggregation is dealt with directly within. Typical services such as ticket booking, hotel booking are associated with this company. Using application software such as Cloud AutoML and Cloud Vision API keys are used by this organization. They have a variety of cloud-based software that helps in managing customer data and the creation of instant bookings. It is online and helps in managing the travel-related concerns of customers. Customers are highly benefited as they come up with systematic management of components.

Goibibo is also associated with basic usage of the programs such as word processing software, Desktop publishing software, integrated programs, and spreadsheet software. Word processing and spreadsheets products come under Microsoft Package for the organization to make sure a complete usage is done.

Ethical and social issues increasing in the digital world

Any data that is shared with Goibibo is safe with them. The storage is done in databases and there is no option of leakage of data to other booking sites. GoIbibo focuses on one of the fastest refund processes to make sure that customers find their services satisfactory (Casino, Dasaklis, and Patsakis, 2019). A range of hotels, flights, and trains are provided to the customer and the application encourages faster payments. Some digital ethical issues can be explained in terms of:

Misusing personal information

Lack of oversight and ownership

Autonomous technology

Moral uses of data and resources (Chen et al. 2018)

Workplace diversity concerns and equal opportunity threats

Digital ethics and priority is defined in terms of

Asking permissions from users regarding the collection and storage of data about users

Taking permission from users before selling their personal data

Giving users a right that requests whether the data is deleted about them or not

Users are given access to personal data on collection storage

Responsible and ethical ways of data storage are organized that are given a chance to be governed and ethics determine the right and wrong view to share the components in cultural values systems and social agreements (Grover et al. 2018). Participant privacy, confidentiality, and anonymity are three core issues raised online. In the case of GoIbibo, there is a significant chance are observed of the data being leaked to other travel websites. Hence, data security is identified to be the first and foremost priority for all concerned people.

Importance of ethical issue finding in the digital world

Ethical issues finding the digital world are associated with the management of customers. All application software used in the digital world gains ethical considerations of data safety and managing privacy. Database and multimedia software is engaged within the development of customer trust and gaining their massive support to become a superpower and run their business smoothly (Heyes et al. 2018). Uses of ERP software for the organization is essentially observed to perform certain actions like human resource management, high degree automation, and better coordination.

Differentiating between networking technologies

Business configurations are associated with virtual tourism networks since the organizational model needs to be rethought. It is one of the most important and permeable structures that organize tourism-related concerns. The destination management system is identified as one of the major concerning components of all time. Tourism attractions, facilities, and overall connectivity are largely concerned with this concept. It relies on the business promotion model that is aired on radio, print, and electronic media. However, there is a particular concern associated with the differentiation of business activities.

personal Area Network and Local Area Network, Wireless Local Area Network is associated within the communication medium and establishes the connection between computers. Such technologies help in the widespread use of networking components. Networking helps in easy file sharing among users and keeps people connected to other devices (Lee et al. 2018). Networking helps in resource sharing such as using network-connected peripheral devices. Telecommunication technologies help in electronic transmission and in the case of tourism, it is highly appreciated and linked. Systems connected within a network help the service providers manage all requests. The customers connected within the local area network helps in the organization of information technology.

Impact of Artificial Intelligence in enhancing the business operations

AI improves personalized customer experience and improves risk management components. From mundane tasks to data analytics the technology always allows companies to the maintenance of a competitive edge. Saving time and money by automating and optimizing processes and different tasks have been observed. The operational efficiencies and increased productivity are associated with artificial intelligence components (Legner et al. 2017). Enhancing the marketing strategy so that more traffic is directed to the website is observed. AI-based chatbots help in the management of customer queries easily and in an improved way so that no customer is dissatisfied.

Artificial intelligence is working largely within organizations since it helps in the overall processing of data essentially. The people across organizations are positively managing working conditions as the API development is observed. Business operations are processed in order to improve age-old practices. Google cloud results in a focused growth to classify and take moderate actions. The average time of publishing photos is lessened to 30 minutes. This is improving business processes (Luftman, Lyytinen, and Zvi, 2017). Goibibo is focused on cloud auto ml and cloud vision API key. All these aspects are created as a part of learning and defining a futuristic tourism industry. Saving time and manpower helps in moderating, annotating, categorizing growth. It expects to increase the image processing components over the last few years.

Different stages of software development life cycle

Different stages of the software development life cycle are "planning, analysis, design, development, testing, implementation and maintenance". Software development life cycle gives clear ideas on goals and does proper testing before installation. Clear stage progression evaluates the membership flexibility and proper testing before installation. It is quite a well-known model where each stage has its own value. Clear stage progression is important since none of the members breaks the project rule (Luftman, Lyytinen, and Zvi, 2017). The best ever SDLC model uses agile technology. Cloud defense is one of the nicest aspects of managing software. In planning the software concept is conceived. The later stages focus on designing the application along with the development and testing of products. Software risk planning and monitoring are also associated with software development tasks. Any potential services that can create an impression on the customer's mind. The risk management process is highly empowered through these software development life cycles. As a common person, it is not possible under the difficulties of the booking system. Hence, it is important to produce quality software through implementing SDLC. Here, an introduction to the agile methodology can be given.

This methodology implies that customers are satisfied through early and continuous delivery. Software working over a comprehensive document allows the software to generate more profit. Customer collaboration over contract negotiation and responding to the change is important. Goibibio believes in working in small teams and getting rapid feedback from customers. The agile method is made more sustainable since it can be collaborative and provide transparency and inspiration content (Ravichandran, Han, and Mithas, 2017). There are daily stand-up practices along with transparency created within the team. The retrospective benefit is associated with continuous improvement. There are customer software demos associated with transparency and customer collaboration.

Analysis and discussion

The chosen organization GoIbibo is investing largely in the software application called cloud AutoML and cloud auto vision API. The potential benefits of these two items are found out to be:

AutuML focuses on freeing up data that focuses on augmenting features as well as productizing the final data pipeline plus model. It helps to get rid of being stuck in the model creation step.

It is the faster and easier step to manage image recognition and continue the drag and drop feature (Rivera, 2020)

Uploading images, managing models, and deploying those models on the cloud becomes easier with this platform and saves lots of time consumed in the picture uploading process

AutoML performs automatic feature engineering and that too from selecting features and creating new features from the existing ones.

AutoML works via using machine learning algorithms and replacing data science

Prediction of clinical outcomes that focuses on pharmacological interventions manages challenges and features engineering services (Schwertner, 2017).

It is an end-to-end process that applies machine learning to practical scenarios.

Automatically and quick discovering of well-performing machine learning model is used for modeling tasks in a predictive way

There are uses of Hyperopt-sklearn, auto-sklearn, and TPOT observed within library functionality

Overall it provides hosted functionality features that trainers each project material to be applicable within engineering

It is also associated with minimizing the search time as there are potential uses of bigquery done

State-of-the-art cloud technologies are effortlessly managed by associating the AutoML. Uses of vision API helps in error-free coding. Moreover, there is a binary classification model that withholds the performance levels of Goibibo as an organization. It finds out routes that are impacting largely upon the business optimization and improvement of cloud functionalities. Smart analytics helps in improved performance optimization and makes a tool used as painlessly as possible (White, 2017). New trainees are needed for the overall development components and the process is fast-forwarded.

There is some drawback of the process such as:

Unsupervised

Reinforcement learning

Complex data type management

Domain knowledge and Data science management

It is one of the most cost-effective solutions that allows hardware resources to be put forward and aligned with company objectives. However, at certain times, advancement can be a threat. This process cannot alter generated solutions. Most of the work is done elsewhere, this results in quality issues as remote operation might not always be successful. This technology is one of its kind and leaves everyone in shock (Viriyasitavat et al. 2020). Cloud automl is a vision that solely operates on artificial intelligence. Now, the concept is quite new for veteran workers and it will take a potential amount of time to learn and accept the technology components.

Conclusion

The study has highlighted different aspects of software application, which anticipates the need of the tourism industry. The use of software applications defines flexibility and applies to the project development concepts largely. As a part of developing a new and iterative approach to software delivery, there is incremental processing. Customer service has become faster and iterative development solutions are also focused throughout the Goibibo tourism concepts. The agile method is identified as a practice that promotes effective planning, organizing components. A full-grown agile manifesto is given the highest priority as there are fully accessible components given into action. As Goibibo looked into the future, it observed opportunities that matched machine learning and AI-driven services. It is becoming one of the largest associative platforms in the tourism sector. Machine learning models and tools give an API suite to the organization that enables business applications to automate.

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References

Journals

Bisong, E., 2019. Google AutoML: cloud vision. In Building Machine Learning and Deep Learning Models on Google Cloud Platform (pp. 581-598). Apress, Berkeley, CA.

Casino, F., Dasaklis, T.K. and Patsakis, C., 2019. A systematic literature review of blockchain-based applications: Current status, classification and open issues. Telematics and informatics, 36, pp.55-81.

Chen, G., Xu, B., Lu, M. and Chen, N.S., 2018. Exploring blockchain technology and its potential applications for education. Smart Learning Environments, 5(1), pp.1-10.a

Grover, V., Chiang, R.H., Liang, T.P. and Zhang, D., 2018. Creating strategic business value from big data analytics: A research framework. Journal of Management Information Systems, 35(2), pp.388-423.

Heyes, G., Sharmina, M., Mendoza, J.M.F., Gallego-Schmid, A. and Azapagic, A., 2018. Developing and implementing circular economy business models in service-oriented technology companies. Journal of Cleaner Production, 177, pp.621-632.

Lee, M., Yun, J.J., Pyka, A., Won, D., Kodama, F., Schiuma, G., Park, H., Jeon, J., Park, K., Jung, K. and Yan, M.R., 2018. How to respond to the fourth industrial revolution, or the second information technology revolution? Dynamic new combinations between technology, market, and society through open innovation. Journal of Open Innovation: Technology, Market, and Complexity, 4(3), p.21.

Legner, C., Eymann, T., Hess, T., Matt, C., Böhmann, T., Drews, P., Mädche, A., Urbach, N. and Ahlemann, F., 2017. Digitalization: opportunity and challenge for the business and information systems engineering community. Business & information systems engineering, 59(4), pp.301-308.

Luftman, J., Lyytinen, K. and Zvi, T.B., 2017. Enhancing the measurement of information technology (IT) business alignment and its influence on company performance. Journal of Information Technology, 32(1), pp.26-46.

Mahesh, B., 2020. Machine Learning Algorithms-A Review. International Journal of Science and Research (IJSR).[Internet], 9, pp.381-386.

Ravichandran, T., Han, S. and Mithas, S., 2017. Mitigating diminishing returns to R&D: The role of information technology in innovation. Information Systems Research, 28(4), pp.812-827.

Rivera, J.D.D.S., 2020. Object detection with a model trained in Google Cloud AutoML. In Practical TensorFlow. js (pp. 163-184). Apress, Berkeley, CA.

Schwertner, K., 2017. Digital transformation of business. Trakia Journal of Sciences, 15(1), pp.388-393.

Viriyasitavat, W., Da Xu, L., Bi, Z. and Sapsomboon, A., 2020. Blockchain-based business process management (BPM) framework for service composition in industry 4.0. Journal of Intelligent Manufacturing, 31(7), pp.1737-1748.

White, G.R., 2017. Future applications of blockchain in business and management: A Delphi study. Strategic Change, 26(5), pp.439-451.