Information Technology And It Ethics Assignment Sample

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Information Technology And It Ethics Assignment

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Task 1: Understanding concepts and techniques used in mobile e-commerce and ticketing

1.1 The way location based services are used

A shift towards an online ordering system has become a strict necessity for contemporary businesses, where consumers can order and pay without visiting shops. One of the fundamental reasons behind this change in food ordering is that customers are inclined towards ordering through mobile applications rather than calling. Li et al. (2020) opined that popularity of food ordering apps is prevalent, as it alleviates face-to-face contact, which is crucial during a global pandemic. In this context, ventures in the food delivery domain often use location-based services to retain an advantageous edge in a competitive business environment.

Location-based services depend on consumers’ smartphones to offer interactive opportunities along with targeted advertisements. According to Alalwan (2020), service organisations are largely developing mobile online food ordering apps alongside e-commerce. Location-based services that are empowered by smartphone technology are considered as one of the novel systems used in today’s businesses of e-commerce and food ordering. These location-based services help consumers to search and find restaurants near them and can deliver in maximum time. Isabela et al. (2018) further emphasized that an enormous calculation persists behind online food ordering and reaching consumers in time. It further shows how ventures of food delivery often use such location-based intelligence to serve offers and discounts to consumers based on their preferences and locations. Real-time geofencing is another emerging tool along with location intelligence, which allows finding consumers within a particular distance of outlets (Sonali et al. 2019). Thus, consumers can take advantage of digital coupons, discounts, and more from ordering from their nearby outlets.

Further use of location-based services in the context of online food ordering and mobile e-commerce is noticed in the contemporary rise of food ordering systems. Users’ location is detected by GPS, which apart from showing nearby service providers, allow consumers to track the entire ordering system. Moreover, customised food ordering system is evident with real-time tracking and consumers’ feedback exhibits convenience for both customers and ventures.

1.2 Design of user friendly mobile application: food ordering

Mobile ticketing application emerges to be highly effective in today’s continuous rise of online food ordering. A friendly mobile ticketing app allows users to ensure transparency while initiating online food ordering. It is indeed that tracking the entire system is significant, and thus, ticket verification can be beneficial to retain authenticity between consumers and outlets from which order is to be delivered (Marquez et al. 2020). For instance, check-in facility in restaurants from the comfort zones of consumers’ homes is fostered in online food ordering systems. Thus, with a friendly mobile ticketing application, codes for verification can be confirmed before booking.

Designing a mobile ticketing application will require consumers to sign in or log in to enter, while certain steps are to be followed for successful food ordering. After selecting an aspired product, the location is to be selected, after which ticket verification has to be performed. An OTP system is to be ensured for ticketing verification before booking them in carts. QR scanning option can be an added advantage in design of this mobile application of online food ordering.

Task 2: Understanding psychology of marketing

2.1 Factors affecting buyers’ purchasing decisions

A number of factors incorporating social factors, cultural, and psychological factors, and more can influence buyers’ purchase decisions. In today’s business environment in e-commerce, consumers tend to initiate online shopping. Nevertheless, online purchasing intention and decisions are subject to varied factors starting from convenience, security to service quality, and others. Irawan (2018) has stated that the theory of purchasing decision making model has explored about the steps like problem recognition, information search, alternative evaluation, decision making and post purchase evaluation. It can be propounded that convenience has a positive influence on consumers’ purchasing decisions through problem recognition, which is largely derived from information search in e-commerce platforms. It further indicates that service quality and trust have a positive impact on online purchasing decisions, while security can have a negative influence on buyers’ purchasing intentions as the part of alternative evaluation and purchase decision. For example, trust in a specific cafe or restaurant allows consumers to purchase frequently from particular shops.

Online food delivery has become a common subject among consumers, as demanding working hours have left little room for a kitchen. Weisstein et al. (2017) illustrated that negative reviews emerge to be a significant influencing factor to affect purchasing decisions in online food delivery. For instance, a negative review regarding a particular restaurant or cafe enables consumers to avoid particular outlets. These are a handful of factors, which affect buyers’ purchasing decisions in online food delivery. Furthermore, factors such as offers and discounts, restaurant reviews, faster home delivery, availability of food also shape consumers’ purchasing intentions in online food delivery (Pinto et al. 2021).

Analysing purchasing decision process

Purchasing decision process in online food delivery follows five distinct steps, which incorporate need recognition, searching information, alternative evaluation, purchase decision, and post-purchase behaviour. According to Rittiboonchai (2021), purchase decision of online food through e-commerce and multi-channel marketing are conducted by a combined influence of information search, evaluation of alternatives, and purchasing decisions.

Need recognition: It is the first step concerning need for food under demanding working hours, which shapes consumers’ intention to buy.

Information search: Past experiences of tastes and quality force consumers to search for information regarding food, which determines actual purchase.

Evaluation of alternative: Wide ranges of restaurant options allow consumers to seek alternatives while initiating online food purchase (Qazzafi, 2019). Rating with regard to reviews plays a key role in this context.

Purchase: It is the fourth stage in buyers’ purchasing decision process, where a buyer attempt action after collating relevant information.

Post-purchase behaviour: It reflects retention of consumer loyalty through enhanced food quality and services such as discounts, and others.

Impact of internal and external influences on buying decisions

Lifestyles, personal thoughts are some internal influences, which often force consumers to incline towards healthy food habits and avoid junk foods. On other hand, external factors such as socio-culture influence consumers’ behaviour, and purchasing decisions (Stankevich, 2017).

2.2

Eye-tracking is a technology, which allows businesses to monitor visual behaviour of consumers on websites and particularly in e-commerce. Dospinescu and Perc?-Robu (2017) illustrated that e-commerce sites often use eye-tracking technology to monitor interactivity dimensions. Hence, consumers’ attitudes towards offered products in restaurants can be investigated, and adjustments related to product and service quality can be conducted. This technology is largely used by e-commerce to grasp consumers’ interests towards products. View of products in a catalog can also be conducted by this technology (Sari et al. 2018). Thus, by predicting consumers’ product selection in e-commerce, particular product discounts and more can be served to gain a wider consumer base.

Task 3: Understanding use of AI in image recognition

3.1

Analysing use of image detection, recognition, and classification in e-commerce

Image recognition is a type of AI automation, which is intended to recognise objects, people, places, and more. Image detection exhibits a computer technology, which processes images, and thus, detects objects in it. Contrarily, image classification labels objects in an image and sorts them by certain classes. It is an advanced version of image detection. E-commerce largely uses image recognition to alleviate inappropriate content in images often uploaded by consumers for reviews. These technologies have been widely used by e-commerce sites to identify explicit items present in websites, and thus, an improved website for businesses can be ensured (Li and Li, 2019). Furthermore, prevalence of use of image recognition is noticed, as it helps in interactive marketing. Solutions derived by these AI technologies assist in identifying consumers’ emotions and facial expressions towards particular products.

Image classification plays an integral role in rising e-commerce, which allows one to detect macro-categories under the umbrella of a macro category. With an exponential increase concerning amount of commodity image data, effective and improved accuracy of image databases is essential (Zhang, 2020). For instance, in a macro category of ‘safety gloves’, several micro categories can persist such as ‘chrome leather gloves’, ‘lather wielding gloves’, ‘industrial leather gloves’, and more. Thus, an improved business can be facilitated by e-commerce by classifying numerous products from categories. E-commerce sites intend to ensure consumers’ convenience by addressing image classification for classifying products, and thus, improve business through interactivity.

Implications of using image recognition to find inappropriate content

Implementation of image recognition is largely used by e-commerce concerning an increasing online purchasing behaviour among consumers. According to Gandhi et al. (2019), different distributions of nudity and explicit content can be highly offensive for e-commerce, which in turn, can disrupt businesses. Inappropriate content largely related to explicit, negative, and nude content can be detected by e-commerce sites using image recognition technology, and thus, can improve e-commerce.

Use of image recognition in eliminating counterfeit products

Image recognition based on machine learning appears to be an effective AI service, through which counterfeit products can be detected, and hence, can improve businesses. Daoud et al. (2020) opined that fake product detection is an integral activity to be performed by e-commerce to ensure an uninterrupted business process. Moreover, retail solutions are designed with AI technologies such as blockchain, ML, and others, which detect counterfeit items. Besides, AI can identify logos with use of a logo detection system, which fosters consumers having right products.

3.2

Augmented Reality (AR) in e-commerce provides consumers immersive buying experiences, where real-time interactions with desired products can be attained. For example, a leading beauty brand, Loreal Paris’s AR filters allow consumers to try different shades through ‘signature faces’ from consumers’ comfort zones, which has accelerated its sales and conversion rates (Thedrum.com, 2019). Conversely, Virtual Reality (VR) in e-commerce allows consumers to interact with a product and see them from all angles. It enables businesses to create touch-and-feel experiences of shopping for consumers (Martínez-Navarro et al. 2019). For instance, the e-commerce venture, Shopify has integrated VR with 3D model to serve immersive shopping experiences to consumers. However, VR is a relatively new concept, and thus, AR seems to be more beneficial for e-commerce.

Task 4: Understanding social commerce

4.1

Social commerce is a subset of e-commerce, which uses peer-to-peer communication, social networks, and online media to reinforce social interactions. It is intended to accelerate online buying and selling of goods and services. Lin et al. (2019) illustrated that consumers in contemporary times rely on social commerce to shape their purchasing decisions. Social commerce can be of various types, which include group buying and daily deals, user-review websites, participatory commerce, social network-driven sales, platforms for peer-to-peer sales, and pick list sites. These are the various types of social commerce, which bring forth distinct features. Elements of social commerce involve merchants, consumers, platforms (social media), and context.

Review-based purchase in social commerce shows a feature of conventional word-of-mouth, which mirrors consumer reviews in contemporary times of e-commerce. Han et al. (2018) emphasized that participatory commerce allows building a community with social proofs featuring numerous likes. Thus, consumer engagement is ensured, which drives sales of the venture. Moreover, social-network driven sales show a feature of multiple tagging, where photos of multiple products can be derived in social platforms such as Instagram. In addition, platforms for peer-to-peer sales also demand consumers to use authentic social media platforms such as Facebook, Instagram, and more. Social marketing is largely done on these platforms through social commerce, which undeniably boosts sales and performance of businesses.

4.2

In today’s rising online shopping behaviour under prevalence of e-commerce, micro-influencers play a key role. They are regarded as highly effective to drive consumers’ purchasing decisions in social commerce. Foos (2020) propounded that often consumers choose social media platforms such as Pinterest, Instagram, and more. In this regard, Gen Z and Y tend to be in line with global trends in fashion. Hence, micro-influencers’ guidance regarding a product is often considered by consumers in social commerce, which drives sales. Moreover, brands use micro-influencers for social media marketing, which intends to draw consumer attention and retain (Isyanto et al. 2020). Furthermore, in-app purchasing exhibits buying of products and services from an inside application within a device such as a tablet or a smartphone. In the context of social commerce, in-app purchases through social media platforms as well as social networks help to build a community, and thus, sales can be accelerated.

4.3

Soldsie is a social e-commerce platform and the first social shopping service, which allows one to track inventory, sales, and data effectively. It allows contemporary retailers to run successful sales on social media platforms such as Instagram and Facebook. The platform of Soldsie largely differs from eBay, which is a leading multinational e-commerce. A fundamental distinction between Soldsie and eBay is that the former allows businesses to sell on social platforms such as Facebook utilising Facebook comments, whereas eBay is a conventional e-commerce platform (Kim et al. 2018). There are some other platforms such as Groupon, The Fancy, and Kickstarter. Groupon helps to connect local merchants with subscribers by offering various activities. The Fancy is a social photo-sharing webstore, where users can engage in socially oriented shopping via picture feeds, while Kickstarter is a funding platform for creative projects (Kickstarter.com, 2021). Thus, all these platforms serve distinct purposes for consumers. In this context, Pinterest can be used to encourage conversion and in-app purchases. Pin engaging, creative content alongside a marketing strategy will be effective for the same.

References

Alalwan, A.A., 2020. Mobile food ordering apps: An empirical study of the factors affecting customer e-satisfaction and continued intention to reuse. International Journal of Information Management, 50, pp.28-44.

Daoud, E., Vu, D., Nguyen, H. and Gaedke, M., 2020. Improving Fake Product Detection Using Ai-Based Technology. In Proceedings of the 18th International Conference on e-Society (ES 2020).

Dospinescu, O. and Perc?-Robu, A.E., 2017. The analysis of e-commerce sites with eye-tracking technologies. BRAIN. Broad Research in Artificial Intelligence and Neuroscience, 8(3), pp.85-100.

Eapen, S.T., 2020. Effective Presentation of Apparel Products on Ecommerce Platforms. Psychology and Education Journal, 57(9), pp.1933-1937.

Foos, A.E., 2020. Teaching Generation Z Social Media Marketing: A Micro-Influencer Project. Journal of Instructional Pedagogies, 24.

Gandhi, S., Kokkula, S., Chaudhuri, A., Magnani, A., Stanley, T., Ahmadi, B., Kandaswamy, V., Ovenc, O. and Mannor, S., 2019. Image matters: Detecting offensive and non-compliant content/logo in product images. arXiv preprint arXiv:1905.02234.

Han, H., Xu, H. and Chen, H., 2018. Social commerce: A systematic review and data synthesis. Electronic Commerce Research and Applications, 30, pp.38-50.

Irawan, I.A., 2018. Effect of trust, convinience, security and quality of service on online purchase decision (consumer case study in tangerang selatan area). Jurnal Ekonomi, 23(1), pp.114-122.

Isabela, E., Drona, J., Fadhilah, N., Tanoto, D.F., Harefa, J., Prajena, G. and Chowanda, A., 2018. NYAM: An android based application for food finding using GPS. Procedia Computer Science, 135, pp.393-399.

Isyanto, P., Sapitri, R.G. and Sinaga, O., 2020. Micro influencers marketing and brand image to purchase intention of cosmetic products focallure. Systematic Reviews in Pharmacy, 11(1), pp.601-605.

Kickstarter.com, 2021, Kickstarter and Taxes, Viewed on 13/11/2021 <https://www.kickstarter.com/help/taxes>

Kim, K., Oh, W., Lim, G., Cha, E., Shin, M. and Kim, J., 2018. The way to make training data for deep learning model to recognize keywords in product catalog image at E-commerce. Journal of Intelligence and Information Systems, 24(1), pp.1-23.

Li, C., Mirosa, M. and Bremer, P., 2020. Review of online food delivery platforms and their impacts on sustainability. Sustainability, 12(14), p.5528.

Li, G. and Li, N., 2019. Customs classification for cross-border e-commerce based on text-image adaptive convolutional neural network. Electronic Commerce Research, 19(4), pp.779-800.

Lin, X., Wang, X. and Hajli, N., 2019. Building e-commerce satisfaction and boosting sales: The role of social commerce trust and its antecedents. International Journal of Electronic Commerce, 23(3), pp.328-363.

Marquez, A., Cianfrone, B.A. and Kellison, T., 2020. Factors affecting spectators’ adoption of digital ticketing: the case of interscholastic sports. International Journal of Sports Marketing and Sponsorship, Vol. 21 No. 3, pp. 527-541.

Martínez-Navarro, J., Bigné, E., Guixeres, J., Alcañiz, M. and Torrecilla, C., 2019. The influence of virtual reality in e-commerce. Journal of Business Research, 100, pp.475-482.

Pinto, P., Hawaldar, I.T. and Pinto, S., 2021. Antecedents of Behavioral Intention to Use Online Food Delivery Services: An Empirical Investigation. Innovative Marketing, 17(1), 1-15.

Qazzafi, S.H.E.I.K.H., 2019. Consumer buying decision process toward products. International Journal of Scientific Research and Engineering Development, 2(5), pp.130-134.

Rittiboonchai, W., 2021. Decision–making process for purchasing organic vegetable products through electronic commerce systems and multi–channel marketing. Interdisciplinary Research Review, 16(5), pp.20-26.

Sari, J.N., Nugroho, L.E., Santosa, P.I. and Ferdiana, R., 2018. The measurement of consumer interest and prediction of product selection in E-commerce using eye tracking method. Int. J. Intell. Eng. Syst, 11(1).

Sonali, P., Priyanka, S., Megha, S. and Pratima, T., 2019. Digital Food Ordering System using Android for Mess. International Journal of Research in Engineering, Science and Management,2(4).

Stankevich, A., 2017. Explaining the consumer decision-making process: Critical literature review. Journal of International Business Research and Marketing, 2(6).

Thedrum.com, 2019, Conversion rates triple when L’Oréal uses AR tech to showcase products, Viewed on 13/11/2021 <https://www.thedrum.com/news/2019/07/02/conversion-rates-triple-when-l-or-al-uses-ar-tech-showcase-products>

Weisstein, F.L., Song, L., Andersen, P. and Zhu, Y., 2017. Examining impacts of negative reviews and purchase goals on consumer purchase decision. Journal of Retailing and Consumer Services, 39, pp.201-207.

Zhang, X., 2020. Content-based E-commerce image classification research. IEEE Access, 8, pp.160213-160220.

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