Fashion Retail Research based on Artificial Intelligence  

Fashion Retail Research based on Artificial Intelligence

1. According to the highlighted comments in the 2 table, modify section5 and section 3.1
2. Write an Abstract

Abstract Includes:

– Intro/ Current trends / why this is an interesting topic to read about
– Your research question
– Your theory (I believe that x will do something to y because…)
– Your proposed methods you will use to prove your theory

Abstract format:

– ½ to 1 full page long
– Double spaced
– Comes after the title page, before the table of contents
 

1. Introduction

1.1 Broad Issues and Current Conditions

The fourth industrial revolution is enabling fashion retailers like many other sectors to use artificial intelligence to increase its capacity to forecast future trends and enhance the customer experience.  Defined as a technology that uses computer programs or algorithms to imitate human thoughts or actions, artificial intelligence is developing rapidly and is now part of our everyday life. Artificial intelligence analyses data from a wide range of sources to learn how to adapt to a wide range of tasks. The current audiences of this study are both small and large organizations in fashion retail. Organizations in the fashion industry have started using numerous types of artificial intelligence applications. Artificial intelligence in fashion retail is mainly supported by the need for customer personalization and big data availability. Many services in fashion retail are currently not feasible without utilizing artificial intelligence. Top leading fashion brands invest in artificial intelligence and machine learning technologies to remain competitive and relevant in the marketplace. According to McKinsey Global Fashion Index, the top 20 percent of the worldwide fashion brands are generating 145 percent of the fashion sector profit (Thomassey& Zeng, 2018).  This implies that any fashion brand that needs to be profitable enough in the fashion industry must invest in artificial intelligence and machine language technologies.

One technique that is being used in the fashion industry is image recognition. This technology recognizes specific objects, animals, people, or other targeted subjects using machine learning and algorithms. The image recognition application is trained using massive data to differentiate between different images (Liang et al., 2020). Artificial intelligence helps organizations to endorse products, increase sales, and enhance customer experience. Artificial intelligence predicts what specific customers will purchase and easily monitor trends that the other customers will buy. This is made possible by collecting consumer data through social media. The data is combined with past data on customer behavior and market performance to determine the products that are in high demand. This enables these organizations to be responsive to the market and the needs of customers. Many organizations are also utilizing artificial intelligence to manage inventory using artificial intelligence tools to gauge demand. Chatbots are being used to help customers in their purchasing experience by offering consumers recommendations to help them make the right purchasing decision (Thomassey& Zeng, 2018).  The chatbots using fashion brands are also used to gather data concerning consumer needs and reasons for purchases. The online shopper searching for a new dress or shoe can interact with the chatbots via mobile app or website.

The real-data analytics enabled by artificial intelligence gives organizations access to essential market data allowing them to view the entire global market from patterns, prices, colors, and shapes. This enables them to establish what to stock and the price of each stock. Other organizations use analytics that combines aspects like consumer feedback, e-commerce sell-through, social media activities, and queries to establish what is likely to become a trend.

Research on the use of artificial intelligence in fashion retail is imperative since organizations must understand customer needs if they need to design better apparel. To understand consumer needs, they must use artificial intelligence and big data from different sources to identify consumers’ preferred textures, size, colors, and other style preferences. Fashion retailers can now quickly establish changing fashion trends depending on seasonal demands and then provide the latest clothing to retail shelves.

• Problem Definition

One big problem facing the fashion industry is consumer dissatisfaction with the products offered. With the industry being remarkably dynamic and customers’ needschanging every time, there is a high chance of making the wrong choices in establishing pricing or making various decisions. One way to improve decision-making and identify consumers’ needs and current trends is to use artificial intelligence. Artificial intelligence helps organizations in forecasting clothing trends and users’ preferences. The retail organization can establish the right price, right product, and right place by understanding the trends and user preferences. The fashion designers are required to develop a diverse and wide amount of fashion products more cheaply and quickly.  The need for quicker fashion design is made possible by big data analyzed to determine the evolving consumer behaviors. It is a good idea for fashion retail organizations to know consumer needs.

Personalization is another issue that needs to be solved by artificial intelligence. Younger generations prefer products that are personalized according to their individual needs.  Personalizing an outfit is a complicated process that entails theme selection, getting the right fit, selecting the right accessories, select colors, and matching cloth pieces. Besides, artificial intelligence is needed in the retail industry to guide pricing, enabling organizations to limit and maximize profits depending on different factors. For instance, if there is a big football game, an organization can increase the team garment price since many fans will want to wear apparel during the game (Pupillo, 2019). There are massive accurate data sources that organizations can gather a wide range of data on different factors when utilizing artificial intelligence. The data can be combined with the organization’s past data to influence the process of decision-making.

• Purpose of the study

The purpose of the study is to explore the usage of artificial intelligence in the fashion industry. According to Pupillo (2019), artificial intelligence in the fashion industry is a human-centered technique to creativity and innovation that incorporates consumers’ needs and the requirements for business success. The study will explore how artificial intelligence continues to revolutionize fashion retail by providing critical intelligence. With the power of artificial intelligence, fashion organizations can gain insight into purchase patterns, fashion trends, and customer preferences. Predictably, Artificial intelligence will change the fashion industry by enhancing business operations and adding more intelligence to facilitate decisions. The artificial intelligence algorithms and programs can collect data from a wide range of sources, including images, videos, and social media (Thomassey& Zeng, 2018). This data is utilized in detecting current trends in a precise and objective manner that would be impossible without using AI. Apart from trends, more specific design characteristics such a shape, length, patterns, length, and designs vital in outfit designs can be obtained from the data collected. There is also a need to monitor consumers’ activities when shopping and establish their sentiments to determine the types of products they prefer and purchase and those they ignore. The study will discuss how artificial intelligence in fashion retails provides automated solutions to monitor these shopping customers. Brands need to be well informed with all trends and predict customer preferences since there is a lot of consumer data source compared to some decades ago. The retailers should respond swiftly to the demand and tailor design clothes accordingly (Thomassey& Zeng, 2018). This task would be impossible without artificial intelligence.

• Research Question(s)

Myresearch question is, s it more effective forecasting for fashion companies that do use AI than for those that don’t?

Organizations that adopt artificial intelligence gain numerous benefits. Artificial intelligence helps fashion industry organizations to forecast trends and establish patterns that can help them design new outfits. Artificial intelligence enables these organizations to analyze purchases, shopping history, likes, shares, and page clicks to determine current and future trends. Artificial intelligence helps organizations to accurately evaluate and forecast what the future holds for specific styles. For instance, AI can enable organizations to establish which prints will not work in upcoming days and which color will be more in demand in upcoming days.  This information can help the organizations predict and prevent manufacturing large surplus amounts of clothing that consumers may not accept or that may never be sold.

Fashion organizations using artificial intelligence can get insights to make the right decisions to provide clothes tailored to consumers’ needs.  The patterns and designs with the appropriate combination of color are critical factors in designing attractive clothes among customers.  Artificial intelligence can recognize new trends and establish new trends hence minimizing forecasting errors. Artificial intelligence can enable designers to keep pace with the ever-changing trends and patterns. The artificial intelligence algorithms can analyze designs through images and copy common styles. Without artificial intelligence, organizations would not have made forecasts at aggregate levels, but now, artificial intelligence can enable them to combine internal and external data from different sources and forecast trends and other future factors (Pupillo, 2019). Artificial intelligence provides insights that would otherwise be impossible to capture in organizations that don’t use artificial intelligence. For instance, an organization using AI can learn that 50 percent of blouse bought in the United States contained a lace trim around the sleeve in the last two years. This information can enable the organization to forecast what consumers are likely to purchase in the future.

• Theoretical Framework

This research used quantitative data obtained from secondary sources. Several articles were thoroughly reviewed to answer the research question posed by this research paper.

The alternative hypothesis used is, it is more effective for fashion companies that do use AI to forecast. The null alternative hypothesis used is not effective for fashion companies that do use AI to forecast.The dependent variable is forecastingforfashion companies, while the independent variable is artificial intelligence usage. The effectiveness or ineffectiveness of the dependent variable (forecastingforfashion companies) depends on whether they use artificial intelligence. The research found that the companies that use artificial intelligence are more effective than those that don’t. These fashion companies use artificial intelligence to determine user preferences and then design clothes based on their shape, weight, height, and size.

The moderating variables are the people acceptable to artificial intelligence and the financial cost of artificial intelligence usage. The moderating variables have a conditional or contingent effect on the relationship between dependent and independent variables. Even if the use of artificial intelligence (independent variable) has a positive outcome, if people don’taccept AI or the financial cost of usage of AI is high, it could be ineffective to use artificial intelligence in fashion companies making the alternative hypothesis to become wrong.

The mediating variable is: Need more training for people to know how to use artificial intelligence. The mediating variable is a variable between the dependent and independent variables, and it is expected to describe the relationship between the dependent and independent variables (Mackinnon, 2015). If people are not trained to use artificial intelligence in fashion companies, the outcome may not be positive. However, if people are trained to understand how to use artificial intelligence, the outcome of artificial intelligence usage will be positive.

2. Literature Review
   • Introduction

By examining different literature from different authors worldwide, this literature review intends to provide an overview of fashion retail’s artificial intelligence from different viewpoints. This literature review was organized into different sections. The sections highlight different themes, such as the combination of AI and data, the predictive analysis to forecast trends, and the importance of artificial intelligence in improving organizations’ revenue and enhancing customers’ experiences. The existing literature has exploredthe state of the art on AI in fashion retail in a concise and integrated manner.The research on AI in the previous decades has significantly enhanced the performance of both service and manufacturing systems(Liu et al., 2019). Most of the research reviewed concluded that artificial intelligence provides an automated solution to fashion retail by streamlining their operation and enhancing customer’s experience.

Literature Review

Duan et al. (2019) defined artificial intelligence as a machine’s programming to exhibit characteristics associated with the human mind, such as problem-solving and learning. Artificial intelligence-based systems can be embedded in hardware devices (such as autonomous cars, drones, advanced robots, or Internet of Things applications). It can be purely software-based (e.g., face recognition system, search engines, image analysis software, and voice assistant).  These AI systems store past data and use it to solve new solutions, or they programmed with a specific set of instructions, which they apply when finding a solution to a problem.  Artificial intelligence is a predominant area that can be applied in different areas. The enormous impact of AI has been used in transforming fashion retail in the last decades. Nowadays, artificial intelligence is incorporated in retail fashion to ensure a diverse number of fashion products in the market (Kim & Lee, 2018). Besides, artificial intelligence helps fashion designers develop products tailored to consumers’ needs and match the newest fashion trend.  Artificial intelligence in the fashion industry helps develop innovative techniques, enhance customer service quality, and enhance overall efficiency.

According to Giri et al. (2019), the main reason why fashion retail has adopted artificial intelligence is because of the consumers’ dissatisfaction with the garments presented by the industry in terms of style, color, and size. Therefore, the retail fashion industry must become consumer-centric to improve customer satisfaction. With the emergence of digitization and globalization, artificial intelligence has gained attention to connect business globally. The application of artificial intelligence has been recognized in fashion retail at different stages, such as cloth design, forecasting sales production, pattern making, and supply chain management.

How Data and Artificial Intelligence Work Together

According to Duan et al. (2019), data is the foundation for artificial intelligence and machine learning. With the development of the internet, mobile devices, social network platforms, and other new technologies that can collect data, fashion retailers have easy access to a wide range of data sources such as social media comments, website feedbacks browsing history, and website click-through rates (Duan et al., 2019). All this data is useful in fashion retail. For instance, consumers’ potential demands and interests and consumers’ sentiments towards products and brands can be used for personalized service design, product recommendation, trendspotting, future product designs, and decision making.  To succeed in the current competitive and data-filled world, fashion retails must extract useful insights from collected data and transform the insights into actionable practices (Kim & Lee, 2018). The fashion industry is rapidly changing due to unpredictable customer demands and the short life cycle of fashion items.  This rapid change necessitates fashion retailers to change their strategies according to technology innovations and market demands to compete with the competitors in near real-time.  These days, numerous types of fashion data are available, and they are semi-structured, structured, or unstructured (e.g., image and text data). Due to the enormous amount of data generated every second, if an organization cannot use them in a real-time manner, the data will lose value.  As Duan et al. (2019) state, artificial intelligence requires organizational flexibility and highly capable technologies.  Therefore, organizations should strive to use the data they have to gain insights into market trends.

AI in Fashion Design

The patterns and design with the right color combinations are the vital factors considered when designing clothes to make them attractive. The fashion industry uses machine learning and artificial intelligence for image recognition to quickly analyze and gain insights into the enormous retail market data. Artificial intelligence can detect trends with demand in determining new trends and minimizing errors. Trends in fashion retail change rapidly as new patterns and designs are introduced in the market every day.  Designers are therefore required to stay informed of the latest styles. To give fashion retailers up-to-date fashion trends, artificial intelligence can collect data from e-commerce sites and analyze designs through images to determine the most popular designs (Kuksa& Fisher, 2017).  Artificial intelligence can utilize social media sites to establish trends, helping businesses to enter the market with the most popular styles.

According to Gu et al. (2020), artificial intelligence is used in retail fashion to ensure the prices are right. Big data tools help retailers have competitive pricing strategies by combining machine learning and artificial intelligence. Retailers incorporate these APIs into a brand’s data to have a rule-based engine that ensures prices are optimized based on real-time external factors such as discounts, competitors, out of stock situations, and inventory (Wong, 2018). This incorporation enables retailers to optimize their prices by ensuring they sell their products cheaper or at the same cost as their competitors.

According to Shi & Lewis (2020), organizations can analyze datasets using artificial intelligence to determine what sells well in the market and recommend or create an entirely new market design. For instance, retailers like Walmart and Amazon are using artificial intelligence-based systems that identify spot and design fashion trends that consumers prefer to purchase.

Artificial Intelligence in Fashion Retail, Fashion Store, and Supply Chain

In fashion, clothes manufacturing is a labor-intensive industry. There are many repetitive tasks, from sewing to sorting, in which artificial intelligence can perform faster with better accuracy minimizing the extra cost on employees (Gu et al., 2020). Artificial-enabled robots and machines can quickly sew the fabrics with perfection while at the same time identifying faults in the apparel and offering quality assurance. The artificial intelligence in supply chain and inventory management facilitates the speedup by improving routes and minimizing the logistic supply and shipping costs(Giri et al., 2019). Organizations that use artificial intelligence to automate logistic and supply chain processes ensure faster delivery or establish alternative routes for vehicles disrupted by unanticipated circumstances such as road construction or bad weather.

Artificial intelligence in retail fashion also provides an automated solution to monitor consumers’ activities when shopping to envision their sentimentsto determine the types of products they prefer or ignore most. Artificial intelligence can monitor footfalls in fashion retail or record the consumers’ shopping experience with the option of getting feedback regarding how their experience was to improve their products and services(Giri et al., 2019).  The visual perception based artificial intelligence models helps the store owners to keep inventory records in their store and also classify items helping them to manage the inventory with artificial intelligence-based automated solutions

According to Liang et al. (2020), robots and artificial intelligence are expected to carry out human tasks such as monitoring and analyzing shelf inventory, moving goods from shelves, and delivering to consumers.  Robots and artificial intelligence are anticipated to help fashion retailers improve store operations and cut costs. For instance, shelf auditing robots and aerial drones powered by artificial intelligence can be used to scan shelves and provide computerized monitoring for inventory management and pricing of products (Luce, 2018). Robots will provide personalized service care services to consumers, gather information about customers to understand their preferences, navigate the consumer within the store, and help find products. Besides, the robots are expected to clean up spills and warn people about hazards. Many retailers will more likely utilize artificial intelligence and robots to improve store operations, gather data about the consumer, and reduce store employees’ load.

According to Luce (2018), supply chain planning in fashion retail aims to match supply to demand.  This can be done by delivering garments to the right customers and at the right time to meet customer needs and desires.  Understock can result in lost sales, and overstock results in price reduction. All the two scenarios have an undesirable impact on inventory productivity. To increase inventory productivity, fashion retail uses artificial intelligence to enhance supply chain planning and demand forecasting. Artificial intelligence can direct workflow to automatically adjust and update plans to address an increase or a decrease in demand due to unanticipated and unplanned events such as seasonal shifts and new product introductions. The capability of fashion retails operations to change easily and quickly is another part that artificial intelligence can be a game-changer. Fashion retailers are increasingly utilizing the Internet of Things (IoT) and other smart machines to monitor products’ movement throughout their supply chain(Luce, 2018). Artificial intelligence can apply reasoning and learning capability to help in tracking and monitoring data.  They can then employ the data to extract insights and come up with a recommendation for the best action to handle potentially costly damages and bottlenecks. In the case of bad road conditions or weather, the systems equipped with artificial intelligence can reroute shipments to event interruptions and rebalance stock levels as required (Luce, 2018). By automating these processesthrough artificial intelligence, fashion retail can be better equipped to meet consumers’ needs, deal with unanticipated events with agility, and maintain service levels.

Online Customer experience enhancement through artificial intelligence

According to Khajeh et al. (2016), the increased scale of online fashion personalization would not be easy without artificial intelligence applications.  The most commonly used services to offer personalized online shopping utilizes Chatbot or artificial intelligence smart assistants.   Chatbots and AI smart assistants are virtual devices that interact with consumers through chats and respond to consumer queries assisting them in navigating a range of in-store and online recommending accessories and clothing that best suit that particular customer. Chatbots are categorized into two; scripted and artificially intelligent. The scripted chatbots are programmed to follow a predetermined set of rules; hence they can only answer questions that are programmed to answer (Khajeh et al., 2016). The artificially intelligent chatbots can interpret the human language to come up with a solution that has not been predefined(Khajeh et al., 2016). Besides, there are specialized chatbots used in retail applications that use natural language processing that makes it easy to tailor marketing depending on the linguistic context such as product reviews, customer service contacts, social media posts, and email(Callari et al., 2019).  chatbots can be integrated with various social media platforms such as Facebook and Messager, allowing them to have an enhanced customer experience while shopping online.

The natural language understanding, a subset of artificial intelligence, helps comprehend human language, making fashion retails easy to implement sentiment analysis. The sentiment analysis helps organizations understand how consumers interact through the chatbot and how they feel about a particular product.  Personalized shopping is also made easier using artificial intelligence applications based on virtual and augmented reality and computer vision.  Essentially, the fashion industry is one of the industries that depend on images. Customers can search for images using text input.  Reverse image search allows consumers to search for images using another image. This enables customers to search for similar garments.  Online fashion retailers have developed visual search applications that allow customers to picture any product using their smartphones and compare them. The application brings all the matching products enabling the customer to choose the product of his choice. The enormous amount of data made available by digital technologies in fashion retail helpsconsumers have a strong personalized experience. According toXingxing Zou et al. (2019), the subscription model, which is characterized by recurring and regular payment for the repeated provision of products, is essential in keeping customers fascinated.

Fashion retailers achieve merchandising personalization through artificial intelligence-powered recommendation engines. They offer a personalized, good recommendation based on customer data. The recommendation engines help consumers to filter a huge amount of data they want.  Recommendation engines can be either content filtering or collaborative filtering. Collaborative filtering utilizes information from a big dataset of consumer behaviors and purchases to determine consumer needs (Raj & Gupta, n.d.). The content filtering uses consumer preferences and actions.  If a consumer visits a site and purchases only brown shoes, similar products will be recommended to him during the visit.

AI and Predictive Analytics for trend forecasting in fashion retail

Khajeh et al. (2016) explain that predictive analysis entails using historical data to predict the future. In fashion retail, predictive analysis is mostly used in size recommendations to match a consumer with the right garment size that will fit them correctly. According to Shi & Lewis (2020), billions worth of footwear and apparel merchandise are returned every year due to incorrect sizing and fit. Fit analytics based on consumers’ data such as age, weight, height, and fit preferences gives best-fit recommendations. According toCallari et al. (2019), predictive analysis using AI can determine which consumers are more likely to purchase and are likely to purchase and are not expected to purchase by evaluating the number of times the customer visits the product pages.

Shifting into Artificial Intelligent in Fashion Retail

Fashion retailers are already implementing AI-powered intelligent automation at a very high pace. According to a study conducted by Wong et al. (2013), over 70 percent of management in the fashion industry anticipate their organizations will be utilizing artificial intelligence by 2022. According to Gu et al. (2020), 40 percent of the executives stated their companies are already involved in some kind of intelligent automation. According to Callari et al. (2019), fashion organizations that are not experimenting with the capability of artificial intelligence in their inventory and supply chain need to move at a high pace to if they want to remain competitive. Artificial intelligence represents a significant technological innovation that cannot just enhance but also change how fashion industries conduct their businesses. Artificial intelligence enables the machine to learn, generate suggestions, and developan autonomous decision.

According to Wong et al. (2013), the e-commerce revolution resulted in significant changes in consumer shopping behaviors, which continue to gain momentum in the social media and smartphone period. In the process, consumer demands have changed the consumer and fashion retail products industry. To meet these changing demands, fashion retailers have to utilize technologies to establish consumer preferences, market trends, and shopping behaviors to design products and engage customers in the best way possible.

To understand how fashion retailers are utilizing artificial intelligence today and anticipate its future impact, Shi & Lewis (2020) conducted a survey of retailer executives and 1100 consumer products in the supply chain and inventory in 15 countries. The authors then took a more in-depth look into the impact to establish what fashion retailers would do to address the future opportunities and challenges associated with artificial intelligence in the fashion industry. The survey found that retail and brand executives have high anticipation that artificial intelligence can enhance their organization’s productivity. The survey respondents expect that artificial intelligence capabilities can improve customer experience and increase annual revenue growth by up to 8 percent.

Expecting Efficiency, Gaining Agility

According to Metcalf et al. (2019), an increase in artificial intelligence provides unexpected benefits to fashion retailers.  Executives whose organizations utilize artificial intelligence today are experiencing a high efficiency than organizations that have not fully deployed artificial intelligence in their organizations.

According to Thomassey&Zeng (2018), organizations that use artificial intelligence shows enhanced operational efficiency, expand and extend capabilities, increase revenue growth, reduces costs, enhance customer experience and improve the quality and speed of decision making.

To measure the extent of the impacts of benefits gained by organizations using artificial intelligence, Wong et al. (2013)analyzed the response of retailers executes who were using artificial intelligence and those that were not using. They found that organizations using artificial intelligence could enhance operational effectiveness, drive business performance, and enable insights.

AI Role in increasing Revenues and creating Better Customer Experience

According to Davenport et al. (2019), the top two impacts of artificial intelligence in fashion retail are enhancing consumer experience and increasing revenue growth. There is a direct correlation between the two since, in many instances, an increase in consumer experience increases revenue.  By improving the consumer experience, fashion retailers can bring about new approaches to consumer interaction and engagement(Ren et al., 2018). With artificial intelligence, fashion retail can establish customer’s expected needs at a specific time and determine the right offer to remain competitive.

Organizations can use automated personalized offers and real-time messaging to keep customers engaged and persuades them to buy their product while there are in the appropriate “shopping mode” (Davenport et al., 2019).  Delivering a consistent customer experience across different channels requires a massive number of data sets. Artificial intelligence thrives on information, and the more the information, the better in providing a better customer experience. For artificial intelligence to influence consumer experience, actionable insights need to be shared in real-time. The speed at which artificial intelligence reveals insights enables organizations to make the right decisions to enhance user experience and increase revenue.

2.2 Conclusion

Artificial intelligence provides exciting new possibilities throughout fashion retail. Fashion retailers utilize artificial intelligence in a wide range of applications, such as fashion forecasting and supply chain. Artificial intelligence provides various benefits to fashion retailers.Organizations that use artificial intelligence shows enhanced operational efficiency, expand and extend capabilities, increase revenue growth, reduces costs, enhance customer experience, and improve the quality and speed of decision-making. AI will continue to make the fashion industry more intelligent and smarter in understanding consumers’ opinions and preferences.

3. Research Methodology

3.1. Methodology

I am going to do third party data analysis of free databases. Using third party data analysis is much cost-effective since it uses data that is already available. Free databases contain either published or unpublished data. I used the third part data analysis to answer my research question and evaluate alternative perspectives of previous studies.  My research question is, is it more effective forecasting for fashion companies that do use AI than for those that do not? To get information about my research question, I searched online databases that describe the effectiveness of various retail organizations that use artificial intelligence in forecasting and those that don’t leverage AI. Before using any data, I ensured that I evaluated its relevance. I only used the data that I found suitable, adequate, and reliable. To check the suitability of the data, I carefully studied the nature, scope and object of the original inquiry. If there was any difference, the data remained unsuitable for my research. I avoided using data that had an inadequate level of accuracy.

Bullets

• My DV is forecasting for fashion companies, which I will measure with a nominal variable.
• The nominal level of measurement will entail the amount of artificial intelligence utilized.
• The independent variable(IV) is the artificial intelligence usage.
• Each subject is placed in one of the two categories: artificial intelligence usage or lack of artificial intelligence usage.
• The moderating variable is people acceptable to AI and the financial cost of using AI.
• The mediating variable entails training people to know how to utilize artificial intelligence.
• My population is fashion designers and fashion retailers.