Knowledge Update

Over the past several years, the digital revolution has enabled customers to obtain greater shopping experience. A pleasant shopping experience for customers is essential to the prosperity of retail enterprises (Mainardes, Coutinho, & Alves, 2023). This is due to the fact that a satisfied consumer is more likely to stay loyal and recommend others to purchase from the same seller. All the interactions a customer has with a retailer are considered part of the customer experience, which should ideally lead to a win-win value exchange among the retailer and its clients. Communicating with customers is one of the crucial ways to develop memorable experiences for them (Bonfanti, Vigolo, & Yfantidou, 2021). Effective modes of communication in the digital age include the use of the internet, social networking sites, mobile apps, and electronic mail. Consumers who shop online have learned to expect quick and easy response to any problems that may arise.

Due to the widespread of internet usage among individuals worldwide, retailers have utilized various social media channels and digital tools for connecting with customers. The advent of technology has also made it possible for retailers to gather and analyze consumer data to obtain much deeper and richer insights to guide decisions regarding service-related matters and conversations with customers (Micu et al., 2022). Since mega stores have detailed customer records and can predict what a shopper will buy, they may provide personalized discounts and promotions for them. It is no secret that omnichannel and online stores can monitor customer service and learn from client comments to improve their response to client requests and satisfy their needs (Asmare & Zewdie, 2022).

On whole, consumers and retailers alike benefit greatly from technological advancements that simplify and improve the shopping experience. Its impact can be felt in all facets of buying; it has made it easier, faster, and more pleasurable for consumers. Technology has greatly improved the shopping experience in a number of important ways. First, by eliminating the need to physically visit a store, online marketplaces and smartphone apps have revolutionized the purchasing experience. This convenience helps save time and makes it possible to choose from a wider range of products (Santos & Bacalhau, 2023). Second, client data can be analyzed with the use of algorithms and machine learning to provide customized product recommendations (Davenport, 2018). This makes it much simpler for customers to find products that they might be interested in purchasing. Third, people with disabilities are now able to shop more easily than ever before due to technological advancements such as screen readers, voice assistants, and adapted interfaces (Villegas-Ch et al., 2023). Forth, through Virtual Reality (VR) and Augmented Reality (AR) technologies, customers can visualize things in their actual environments before making a purchase (Bonetti, Warnaby, & Quinn, 2018). This is very helpful in businesses such as cosmetics, clothing, and furniture.

Earlier research also showed that due to the technological advancements, retail stores can better manage their stock and ensure that their products are readily available when and when customers need them (Sanders, 2016). As a result, customers will have a better shopping experience and fewer instances of products being out of stock. In addition to that, customer ratings and reviews posted online aid others in making well-informed purchase decisions (Maslowska et al., 2017). Customers can write about their own experiences and read those of others on digital channels. Anshari et al. (2019) pointed out that by analyzing customer data, stores can better understand their clientele and hence better target their advertising and promotions messages. As most of the customers worldwide have access to internet and use smart phones, customers' shopping experiences can be improved by loyalty programs, which have become more accessible to them through mobile apps and digital loyalty cards. However, retailers should ensure that their website and mobile applications are user friendly, visually appealing, and compatible with a wide range of devices. Finally, in a post-pandemic era, where time is of the essence, technological developments in retail sector like self-checkout kiosks and contactless payments has made shopping more convenient and safer by reducing wait times and minimizing physical encounters (Leng, 2021).

To sum up, digital tools have become an essential component of the shopping experience, providing ease, customization, and accessibility, all of which boost customer happiness and propel retail success. It is likely that as technology continues to advance, it will bring even more innovations to the online shopping, thus enhancing the overall experience of shopping for customers.

References:

• Anshari, M., Almunawar, M. N., Lim, S. A., & Al-Mudimigh, A. (2019). Customer relationship management and big data enabled: Personalization & customization of services. Applied Computing and Informatics, 15(2), 94-101.
• Asmare, A., & Zewdie, S. (2022). Omnichannel retailing strategy: a systematic review. The International Review of Retail, Distribution and Consumer Research, 32(1), 59-79.
• Bonfanti, A., Vigolo, V., & Yfantidou, G. (2021). The impact of the Covid-19 pandemic on customer experience design: The hotel managers’ perspective. International Journal of Hospitality Management, 94, 102871.
• Bonetti, F., Warnaby, G., & Quinn, L. (2018). Augmented reality and virtual reality in physical and online retailing: A review, synthesis and research agenda. Augmented reality and virtual reality: Empowering human, place and business, 119-132.
• Davenport, T. H. (2018). From analytics to artificial intelligence. Journal of Business Analytics, 1(2), 73-80.
• Leng, H. K. (2021). The COVID-19 pandemic and its impact on grocery retailing: a Singapore case study. The Future of Service Post-COVID-19 Pandemic, Volume 2: Transformation of Services Marketing, 49-63.
• Maslowska, E., Malthouse, E. C., & Viswanathan, V. (2017). Do customer reviews drive purchase decisions? The moderating roles of review exposure and price. Decision Support Systems, 98, 1-9.
• Mainardes, E. W., Coutinho, A. R. S., & Alves, H. M. B. (2023). The influence of the ethics of E-retailers on online customer experience and customer satisfaction. Journal of Retailing and Consumer Services, 70, 103171.
• Micu, A., Capatina, A., Cristea, D. S., Munteanu, D., Micu, A. E., & Sarpe, D. A. (2022). Assessing an on-site customer profiling and hyper-personalization system prototype based on a deep learning approach. Technological Forecasting and Social Change, 174, 121289.
• Sanders, N. R. (2016). How to use big data to drive your supply chain. California Management Review, 58(3), 26-48.
• Santos, V., & Bacalhau, L. M. (2023). Digital Transformation of the Retail Point of Sale in the Artificial Intelligence Era. In Management and Marketing for Improved Retail Competitiveness and Performance (pp. 200-216). IGI Global.
• Villegas-Ch, W., Amores-Falconi, R., & Coronel-Silva, E. (2023). Design Proposal for a Virtual Shopping Assistant for People with Vision Problems Applying Artificial Intelligence Techniques. Big Data and Cognitive Computing, 7(2), 96.

In the rapidly evolving landscape of public policy, the integration of artificial intelligence (AI) holds immense potential. AI, which encompasses various technologies like natural language processing and deep learning, can provide invaluable support to governments and policymakers. We can explore how AI can assist in crafting more effective and data-driven public policies, fostering innovation, improving public services, and enhancing transparency.

Data-driven decision-making:

One of the primary advantages of AI in public policy is its ability to analyze vast amounts of data and extract valuable insights. Policymakers can leverage AI algorithms to process information from diverse sources such as social media, surveys, and public databases. This data-driven approach enables policymakers to make informed decisions based on real-time information, reducing the reliance on outdated or anecdotal evidence (Charles, Rana, & Carter, 2022).

For example, during a public health crisis like a pandemic, AI can help track the spread of the virus, predict hotspot areas, and recommend targeted interventions. Such data-driven decision-making can save lives and resources while minimizing societal disruptions.

Policy simulations and scenario-planning:

AI can also be employed for policy simulations and scenario planning (Gilbert et al., 2018). By creating virtual models of various policy options, policymakers can assess potential outcomes and unintended consequences before implementing a particular policy. This proactive approach allows for the refinement and optimization of policies, resulting in more effective and sustainable solutions.

Enhanced public services:

AI can revolutionize public services by automating routine tasks and improving service delivery. Chatbots and virtual assistants powered by AI can handle inquiries from citizens efficiently, freeing up human resources to focus on more complex issues. Additionally, AI-driven predictive analytics can be employed to anticipate service demands, ensuring that resources are allocated where they are needed most (Henman, 2020).

Furthermore, AI can optimize resource allocation within government agencies. For instance, in education, AI algorithms can help distribute funding more equitably based on the specific needs of schools and students, ultimately improving educational outcomes.

Transparency and accountability:

AI can enhance transparency and accountability in government operations (De Fine Licht & De Fine Licht, 2020). AI-driven data analytics can identify patterns of corruption, fraud, or inefficiency within public organizations. This not only reduces the risk of misconduct but also fosters public trust in government institutions.

Moreover, AI can automate compliance monitoring, ensuring that policies are consistently enforced across different regions or departments. This reduces the likelihood of policy implementation disparities and fosters a fair and equitable society.

AI has the potential to be a game-changer in the realm of public policy. By leveraging data-driven insights, facilitating policy simulations, enhancing public services, and promoting transparency, AI technologies can empower governments and policymakers to make informed decisions that benefit society as a whole. However, it is crucial to strike a balance between the use of AI and ethical considerations, ensuring that the technology is deployed responsibly and in a way that respects individual privacy and human rights. As we move forward, embracing generative AI as a valuable tool for public policy can lead to more effective, responsive, and citizen-centric governance.

References:

• Charles, V., Rana, N. P., & Carter, L. (2022). Artificial Intelligence for data-driven decision-making and governance in Public Affairs. Government Information Quarterly, 39(4), 101742. https://doi.org/10.1016/j.giq.2022.101742
• De Fine Licht, K., & De Fine Licht, J. (2020). Artificial intelligence, transparency, and public decision-making. AI & Society, 35, 917–926. https://doi.org/10.1007/s00146-020-00960-w
• Henman, P. (2020). Improving public services using artificial intelligence: possibilities, pitfalls, governance. Asia Pacific Journal of Public Administration, 42(4), 209-221. https://doi.org/10.1080/23276665.2020.1816188
• Gilbert, N., Ahrweiler, P., Barbrook-Johnson, P., Narasimhan, K.P., & Wilkinson, H. (2018). Computational Modelling of Public Policy: Reflections on Practice. Journal of Artificial Intelligence and Social Simulation, 21(1), 14. https://doi.org/10.18564/jasss.3669

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