research paper on fashion trends

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The Trend Research Toolkit

by Gozde Goncu-Berk, Ph.D., University of California, Davis

Introduction

Economic, social, political and cultural contexts or the zeitgeist are impacting trends across industries and demographics or market segments as the boundaries of traditional consumer segmentations and traditional markets are blurring. For instance, female and male interests and behaviours are merging; and scientific developments in nanotechnology are affecting fashion and architecture industries at the same time. Therefore, forecasting fashion trends requires a thorough assessment of the long-term direction of the society and its implications across multiple industries as the first step. Once an emerging megatrend across industries is identified, the next step is to focus on the meaning of that trend for consumers in the context of the fashion industry and to determine the applications of that trend in competition and in industry specific shows and publications. Finally, this data is interpreted and synthesized into product attributes such as function, style, color, materials and textures for future seasons.

Fusion of objective and subjective skills is needed for forecasting future trends. Objective and analytical skills are required for systematic data sourcing, data analysis and interpretation. Trend forecasters should also have more subjective and artistic skills and characteristics such as awareness and intuition to sense newly emerging directions and to make predictions about their future implications. These subjective skills do not come from birth or appear suddenly rather they are built over time based on collection of experiences such as methodological research and constant information flow from networks. Thus, forecasting trends requires using rational research and a well-established network to predict where, when and why new things happen.

This chapter will introduce methods of conducting desk research and building an online trend network to ensure up-to-date constant information flow as well as formal methods of conducting research in the field to gather original data about specifics of a trend.

Desk Research

Step 1: Megatrends and Trend Drivers

• To determine an emerging megatrend across industries
• To determine potential innovators and early adopters of this trend
• To determine target population of consumers for in-depth field research
• To determine experts/knowledgeable professionals for in-depth field research

Websites, Social Media, Blogs, Magazines, Newspapers, Periodicals, TV programs, Radio Shows

Building an Online Trend Network

The first step of forecasting fashion trends starts with desk research where trend forecasters scan and review emerging socio-cultural shifts in economics, politics, science and technology, arts, entertainment and new ideas across industries in online and offline platforms. Although this process is described in a step-by-step manner, this scanning phase is an on-going process where forecasters constantly look for “the new”.

Gazing across online and offline media to detect and track changes in the way people live can highlight megatrends which can be observed in multiple industries, while identifying potential adopters of the megatrend and professionals knowledgeable about the megatrend. Forecasters use different terms such as cross-cultural analysis and cultural brailing to define this process of building insights about a megatrend affecting people on many scales.

Cross-cultural analysis involves scanning across industries to detect traces of emerging threads of commonality. Three-times rule can be used to prove credibility of an emerging trend when conducting this type of a scan. Three times rule is spotting three applications or examples of a trend with noticeable characteristics in three unrelated industries (Raymond, 2010). For example, let’s say you have identified three products with common characteristics pertaining to a trend, then you need to detect three examples of the same trend in three other industries which could be anything from retail, interiors, automotive, product design, beauty, technology, food, packaging or others. If common trends are found, these may constitute a mega-trend. Using three-times rule while conducting your desk research helps you validate your preliminary insights about an emerging megatrend.

Cultural Brailing is a term popularized by influential American trend forecaster Faith Popcorn and her trend forecasting agency “BrainReserve”. It can be defined as being open to anything new with all your senses wherever you are and whomever you are with. Susan Choi, Trend Track director at BrainReserve describes this process:

“Brailling is a way of communicating language through bumps on a page. We take that same technique here and feel the bumps in culture. The bumps are everything. Again it is about using all of your senses: things that you see, things that you taste, things that you hear. For example, it could be a matter of walking into a retail store and noticing the lighting, the music, feeling the different textures, just fully immersing yourself into whatever environment that you are in”

Whether the method you are using is cross-cultural analysis, cultural brailing or another one, according to Raymond (2010) asking the following questions helps while reviewing online and offline resources in order to build accurate insight about a possible megatrend.

Who – Who started the trend? Who are innovators and potential early adopters? What – What would you name the trend?

Where – Where is the physical or virtual space the trend emerges?

Why – Why is the trend emerging now?

When – When was the trend first noted?

Building Trend Network

As a result of the Internet, there is a tremendous amount of already existing research that can guide the trend forecasting process. As a result of the Internet, there is a tremendous amount of already existing research that can guide the trend forecasting process. Some of this information is paid for and some of it is freely available. When we consider the number of online daily newspapers and magazines, blogs, social media and websites dedicated to sharing new ideas, the amount of readily available data can be overwhelming. The first step in managing this amount of data to establish a library of favoured information sources and build an online information network, called a trend network. Such an online trend network can help you scan new ideas, products, services, cultural shifts on a regular basis.

Fashion trends do not take place in a vacuum independent from other industries (i.e. architecture, interior design, product design, food, entertainment, cosmetics and so on). All industries respond to major cultural shifts, the Zeitgeist or the spirit of times. Therefore, it is vital not to limit yourself to the fashion industry or build a network composed of only similar interest groups. People who share similar interests generally know similar things so the more diverse your network is the greater is the diversity of ideas you can reach.

Covering a wide range of online research sources builds a diverse trend network that will result in a need for a system to store, record and access material. One way to manage a library of online daily information flow is using online services like myalltop.com , delicious.com or google bookmarks where you can create a personal and online magazine rack of your favourite blogs and websites.

Collaborative Websites That Scan New Trends Globally

Today, most of the trend spotting websites benefit from crowdsourcing to detect emerging trends. Crowdsourcing is obtaining ideas and content from a large group of online community. Many websites benefit from communities around the world who help to spot new and hot ideas and happenings in their localities. Instead of relying on what a guru trendsetter states, these websites build a collaborative hive where thousands of people all over the world share new concepts they have spotted. The information sent by spotters is usually reviewed by an editorial team and then published in a blog format, often as free daily, weekly or monthly newsletters.

Springwise.com publishes new business ideas, technologies and products from wide range of industries on a daily basis as well as free daily and weekly newsletters.

Trendwatching.com is based in the Netherlands and publishes free monthly trend briefings where they address a megatrend with product and business examples from variety of industries around the world. The website offers a free tool called “consumer trend canvas” that can be used to analyze any detected trend in depth.

Trendhunter.com crowdsources new ideas about fashion, technology, design, business and culture publishing yearly trend reports specific to each industry. The website allows you to filter trends for each industry and also lists top twenty trends of the day, week and month. Interviews with influential professionals and articles about trends are also available.

Jwtintelligence.com publishes trend research and analysis across industries and geographies as well as in depth yearly trend reports.

Trendoriginal.com publishes new ideas in a categorized format such as lifestyle, fashion, tech, science and also offers user-generated directory which maps out the best trend spotting blogs.

PSFK.com is a content network that leverages its broad community and research methodology to create inspiring editorials, videos and events for their readers.

NOTCOT Inc is defined as a network of design sites including notcot.com and innovative community contributed sites notcot.org and notcouture.com . notcot.com visually displays editorial ideas and products in fashion, design, technology, home décor, food and drink while notcot.org displays ideas submitted by trend spotters. notcouture.com is solely focused on latest fashion and beauty for women and men.

Cassandra Daily or trendcentral.com is a free daily email newsletter and web site featuring relevant lifestyle, fashion, entertainment and technology trends and social happenings of the day using research, and insights of the global trendsetter network of The Intelligence Group.

CoolBusinessIdeas.com based in Singapore shares editorial and informer/trendspotter submitted ideas on emerging trends and new innovations at global and local scales. They cover wide range of topics from fashion and design to music, automobiles, retailing and health and beauty. It offers a free weekly newsletter.

Websites and Online Magazines

Coolhunting.com covers innovations in design, technology, style, culture, food and travel in a categorized format as well as weekly videos and interviews.

DavidReport.com is an online magazine and a blog that explores everything from art, architecture, culture, design and fashion to food, innovation, music, sustainability and travel. trendtablet.com is a free social media platform designed and curated by Lidewij Edelkoort where you can see examples of her recent works for clients

trendland.com is a highly visual online magazine covering new ideas in fashion, design, lifestyle, music, art and architecture.

refinery29.com is an independent fashion and style website in the United States covering everything from shopping and beauty to wellness and celebrities.

Trend Forecasting Agencies

Trend forecasting is a very cyclical and on-going process that requires constant research and analysis of new ideas and socio-cultural happenings. Trend forecasting is a very cyclical and on-going process that requires constant research and analysis of new ideas and socio-cultural happenings. Therefore, it takes a lot of time, effort and money investment to forecast trends and many companies fully and partially outsource this type of research and analysis. There are many professional trend-forecasting agencies operating at many different levels. For example, World Future Society and Copenhagen Institute for Futures Studies systematically analyse future trends that will shape humanity. Studio Edelkoort and BrainReserve are companies by two leading global trend spotters Li Edelkoort and Faith Popcorn that operate at macro level and offer trend forecasting services to many different industries. On the other hand, companies like WGSN and Stylesight solely focus on the fashion industry. These companies have staff travelling around the world looking at street fashions and lifestyles, monitoring all sort of online and offline sources to identify the new and the next. Fashion forecasting agencies visit trade shows for colors and textiles and scan fashion merchandise around the globe as well as attending designers shows during fashion weeks. Trend forecasting agencies charge large sums of money for their knowledge. While some forecasting agencies share parts of their information online, full access to their projective reports requires membership.

Copenhagen Institute for Futures Studies (http://www.cifs.dk/en/) identify and analyze trends that influence the future based on statistical analysis and research of interdisciplinary staff from economics, political science, ethnography, psychology, engineering, PR and sociology. Most of the content requires membership.

The World Future Society (wfs.org) investigates how social, economic and technological developments are shaping the future.

StudioEdelkoort (edelkoort.com) , headed by Lidewij Edellcoort, a world-wide known trend guru, offers consultation on trends and product identity to wide range of industries from automobiles and high tech to home environments and fashion. The firm publishes limited quantity trend books targeting fashion, interior and cosmetic. Edelkoort creates audiovisual presentations about upcoming trends twice a year and gives seminars around the world.

BrainReserve (faithpopcorn.com) , led by famous American futurist Faith Popcorn, offers predictions of megatrends that will shape society. The company provides consultation at many levels with publications, seminars and future focused discussions.

WGSN (wgsn.com) was launched in 1998 as a trend forecasting service for the fashion industry and today employs over 300 editorial and design staff in offices throughout Europe, Asia, North and South America, and the Middle East. WGSN provides seasonal coverage and analysis on key looks, colors, and fabrics from all significant fashion week shows and offers specialized insight ranging from denim and intimates to kids wear and footwear to an international clientele such as Marks&Spencer and Target. The company partners with Vogue magazine to offer a digitized repository of every issue of American Vogue since 1892 as well as with the technology company Lectra for a starter pack filled with sketches, patterns and prints to create 3D virtual prototypes.

stylesight.com founded in 2003, has over 3,000 subscribing companies and 40,000+ individual users. The company offers forecasting and trend analysis and a large image library to a clientele from diverse industries such as eBay, Zara, Avon, The North Face and others.

Trendstop.com , Trendbuero.de , Japanconsuming.com are other agencies that offer localized and global trend forecasting services for their clients.

Social Media

The more people you are involved with, the more interesting ideas you are likely to receive. There are many social media platforms that can be utilized for trend forecasting research. When you consider the amount of information that runs in these networks, the number of people you need to be involved with may be overwhelming. Taking advantage of social media for trend forecasting research requires managing it carefully for effective use of time and effort. According to Vanston (2011) a first step in managing social network is selecting the general area of interest to receive new information. The areas of interest should be broad enough to uncover promising ideas but narrow enough to prevent the need for excessive effort. Next steps are to identify subject-matter experts who can provide new insights and then engage and maintain relationship with these people.

Facebook and Twitter can be especially useful to follow experts, influential people, companies and organizations on a regular basis. Pinterest is a significant social media channel where millions of consumers are interacting with millions of websites on a daily basis extracting ideas from the web and sharing them with others. Pinterest is not only useful to build a social network for receiving ideas but also to store and organize the visual information. Instagram is another web based social media platform where original photos are shared on a daily basis. Instagram especially resonates with Millennial consumers more than any other age bracket and thus can be effectively utilized to receive up-to-date visual information on their lifestyles. Tumblr is a microblogging platform. It is used to post short texts, photos, quotes, links, music and videos. Having a Tumblr account is like having a Web page. Users can customize its appearance with themes and get their own URLs that anyone can access online.

All of the websites, blogs and companies listed in this chapter including the ones that require subscription publish from one or several types of social media. Using social media to follow new ideas from a selected set of companies is a useful way of being up-to-date and conducting continuous desk research.

A blog is a personal website where an individual records personal opinions on a regular basis. Fashion blogs can be split into two main categories; independent blogs which are personal postings of an individual or group of people and corporate blogs which are run by a magazine, brand or a store (Rocamora, 2013). Lately, fashion blogs have gained mainstream presence and influence in the fashion industry. Independent fashion bloggers get invitations to fashion events, sell advertisement space to fashion retailers on their blogs, review and promote fashion products. There are numerous fashion blogs that are dedicated to sharing new trends, ideas and street fashions in trend forward places around the world. Google Blog Search is a tool used to search for blogs and blogposts.

thesartorialist.com was launched in 2005 by Scott Schuman who was listed as Time magazine’s Top 100 Design Influencers. He captures street styles throughout New York and Europe as well as fashion shows. Similar blogs for street fashion around the world: streetpeeper.com , facehunter.com , stockholmstreetstyle.com .

thestylerookie.com was started in 2008 by 11 year-old Tavi Gavinson who received a lot of publicity when her blog on teen style became very popular. She was invited to fashion weeks to give talks and style looks for well-known companies.

A few of the popular personal style blogs on women and men’s fashion are themanrepeller.com , theblondesalad.com , cupcakesandcashmere.com , fashiontoast.com , and bryanboy.com .

Other Online Tools

Google zeitgeist reveals the spirit of times by aggregating millions of search queries received every day. Google statistically displays searches done during one year and shows the top ten searches of the year as the current worldwide Zeitgeist.

Google Trends is another statistical tool that can be very helpful when researching megatrends across industries. You can search a specific term and see how many times it has been researched over time. You can specify it according to a country, according to a city. It gives a statistical display of what’s popular and what people are interested in currently.

You can set up Google alerts to monitor a query of your interest. Then, google alerts searches the web such as web pages, news, blogs that match new results for the query. When there are new results, google alerts sends them to you in an email format. Your query can be a topic as wide as fashion or very focused, such as customized footwear.

Offline Media

Although there are many online tools that help you research and store ideas and inspirations of future trends, it is still very important to do it in the traditional way. Following news from physical newspapers and magazines, reading books, following influential TV shows and radio shows are still effective means to understand the spirit of times.

Having a physical trend notebook for documenting your ideas and research is a very useful tool. You may not be online when you have an immediate idea or when you see or read something interesting. You can use a robust notebook and keep it with you at all times to sketch ideas, take notes, make diagrams, attach images and materials, fabrics and trims. Trend notebooks do not have rules set in stone; it is a completely personal tool, almost like a personal diary of the new things you have detected.

Gathering data from different online and offline resources and networks will give you insight about what is new and hot; however it is not enough to predict future trends. You will need to start analyzing this data in order for it to make sense and carry you to the next stage of field research.

As stated earlier, using the three times rules validates that you have identified a credible megatrend. While scanning online and offline media first determine the three examples of the megatrend in the fashion industry and three more examples of the same megatrend in three additional industries.

Next, you will need to identify innovators and early adopters of this trend to be able to focus your research on a specific target population during the field research stage. Based on your desk research so far, you can create a list of demographic, geographic and psychographic characteristics of innovators and early adopters of this megatrend. Demographics are about characteristics such as age, sex, income, marital status, family size, education, religion, race, and nationality; Geographics are about where people live, including information about which country, state, city, and the population in each area. Psychographics are about attitudes, tastes, values, and fears of people.

During desk research you will also develop a list of repeating expert names about the megatrend. These experts are knowledgeable professionals who can provide in-depth knowledge or coherent insights about the megatrend. The experts can be academicians, industry professionals like marketers, designers, retailers, journalists, economists, editors, and psychologists.

Field Research

Step 2: Industry Specific Trends

• To focus on the specifics of a megatrend in the context of fashion industry
• To develop an in depth understanding of the target consumer population
• To develop an understanding about the state of trend in the marketplace and competition
• People (innovators, early adopters, experts)
• Environments (streets, malls, stores, campuses, bazaars, concerts, theaters, bars, pubs,…)
• Observation

Competitive Research

When an emerging mega trend is identified with desk research, the next step is to deep dive and focus on the specifics of this trend by researching its potential impact on consumers for a specific industry. For example, the ageing population is a megatrend and there may be less obvious industry specific trends within this trend such as new materials and sizing system in clothing that is are sensible and aesthetically pleasing to older adults. Field research with consumers and experts related to the identified mega trend can yield valuable original data.

Even though disciplines like fashion, design, retail, marketing or journalism may seem quite diverse, one common factor among these disciplines is that the person is at the center of the profession. Understanding the human component is a vital part of understanding the impact and importance of a trend within a business sector. Today, there is a need for deeper collaboration between creators and individuals. Individuals think of themselves as active participants in the creation process rather than as consumers, customers or users of products. Thus, understanding behavioural and attitudinal needs, expectations and aspirations of individuals in the context of a megatrend are crucial to forecast industry specific trends and develop new products.

It is not a very straightforward task to understand people and especially what people will want in the future. Therefore, conducting field research is much more ambiguous than desk research. For example, if you ask people what type of garments they would like to wear next season, they probably will tell you the things they see around them. It won’t lead to anything new but only a repetition of what is already available. It is your job to discover the unarticulated aspirations, discover the needs people themselves probably are not aware of. You will need to work like a lifestyle detective and your tool in doing this is field research with methods of observation, interview and survey. You will extract knowledge first by observing what people do and asking and then listening to what they say.

Observation: Sensing vs. Looking

Trends have social, cultural or lifestyle aspects that can be observed more effectively than be articulated or described. Observation is seeing and understanding the world through images and then articulating this understanding with words. Like cultural brailing, observation is a transformative experience and requires immersing ourselves with all senses to see, listen and feel the new things and form impressions. Observation also requires freeing ourselves from the biases and prejudices we hold toward people and things.

Observation promotes feeling, thinking and responding toward something without passing judgment, as opposed to simply passively looking at something. When all we do is to look at something, meaning is lost. We look at the screen of our phone or we only look at the penny we use every day. We don’t consider the context beyond this simple act. This also applies to most of the objects we see around us. If asked to draw a quick sketch of a bicycle, you would probably have a hard time remembering all the details. When we look at something, although our mind may be active, we are not fully engaged in creating and embedding every detail to memory. On the other hand seeing something means to “understand” it in a deeper way. The combination of seeing something and understanding at a level at which you can identify all the details is really needed to observe something.

When we observe something, we not only see the big picture but we also see all the elements that unite to create it. As the observer we should be able to briefly take ourselves out of the story, and see the patterns we miss when we are too close to the subject. So observing requires seeing the parts as well as the sum of the parts. Think of it as zooming in and zooming out, zooming in to see the details, to see the leaves and branches of the trees and zooming out to see the bigger picture, the forest as a whole.

There are two formal ways of conducting observations. Non-participant observation involves data collection by observing behavior without interacting with the people and the environment. In this method, the observer is quiet, watching and trying to understand people’s lives, behaviors and environment. Non-participant observation helps to develop a preliminary understanding of the cultural context. Participant observation data are collected by interacting with people and environments, thereby experiencing the phenomenon being studied. Observers actively experience and feel people’s way of life by shadowing them. There are two options in conducting participant observations. Covert observations are when the viewer blends in and identity as an observer is not revealed. The other is overt observations where you reveal your identity as the observer. The advantage of covert observation is people will behave naturally. However, it is open to many ethical concerns as you are covering your identity and actively engaging in an activity with people and not telling them that you are conducting an observation.

In conducting observation one rule of thumb is to never rely on memory, as it is very easy to forget details. It is best practice to get help from notebooks, pens and pencils, sketchbooks, cameras, video cameras and voice recorders. It is also a good idea not to rely on a single method to capture observations. You should use a combination of visual data in addition to notes. You should always physically take notes in addition to digital recording because while you take notes, you not only record what you see but also reflect on what you see. When taking notes it is very important to be descriptive about what you observe rather than to be prescriptive or judgmental. To be descriptive you need to use descriptive adjectives and nouns that help you visualize the thing you are describing. This not only helps to remember but also triggers subconscious insights and associations. For example, in describing a shoe as “a navy sneaker” is shallow and prescriptive. On the other hand, being more thoroughly descriptive can be: “Navy color ankle sneaker with large logo on the side, white thick sole and checkered print behind ankle.”

Trend forecasters tend to be very descriptive about their observations as they have the ability to remember great detail. Raymond (2010) describes several techniques to build a visual memory and recall things effectively when observing a person and an environment. When looking at what a person is wearing including jewelry and body adornments, you can note items by placing the person in north, east, south and west axis. Another way is systematically noting items from head down and from inner layers to outer layers. While observing an environment, you can place it in three by three grid and start noting down characteristics in a clockwise direction from top to bottom and left to right.

In addition to observing consumer behaviour, other environments for observing fashion trends are Fashion Weeks, Trade Shows, and street fashion in the world fashion capitals. Some of the influential trade shows for fashion include:

• Premiere Vision- Textile and fabric shows with around 700 weavers from 28 countries held in Paris and NY.
• Pitti Immagine Filati- Knitwear for men and women, held in Florence, Italy.
• Bread & Butter- Latest trends in street and urban wear, held in Berlin, Germany. Interstoff Asia- Textile and apparel show held in Hong Kong.

An interview can be described as a conversation with a purpose. Interviews are very useful tools for gathering information of an emerging trend from innovators and early adopters and also from experts. In situations where we can’t observe we have to ask people questions. For example, we cannot always observe thoughts, feelings and intentions. We cannot observe behaviors or things that took place in the past. Sometimes there may be private situations that preclude the presence of an observer. In these situations the interview method and asking questions will provide the best opportunity for information gathering.

The traditional methods of interviewing empower the interviewer, as he/she is the person in control of the discussion content and time spent. Interviewers should purposefully develop and plan their interview strategy to bring participants into the partnership and avoid researcher/respondent or the expert/novice perceptions (Beyer & Holtzblatt, 1998). As an interviewer you are not there to fulfill the task of collecting answers to a set of questions from the people, nor as the experts who are there to help the people and answer their questions. Host/guest perception of interviewee’s role is another obstacle in building rapport (Beyer & Holtzblatt, 1998). The presence of interviewers as strangers in the interviewee’s environment may result in the host role for the interviewee and the guest role for the interviewer. In this type of relationship participants may try to please the person conducting the interviews just to make them comfortable.

Building a relaxed rapport is the key for a successful interview. There should be a mutual relationship where both parties are equal, honest and open. A master/apprentice model works the best where master symbolizes the interviewee and apprentice symbolizes the interviewer (Beyer & Holtzblatt, 1998). A master teaches in the context of doing and this way the implicit structure becomes apparent and visible to the apprentice. People usually are not aware of the reason for their actions as they are built based on years of experience, or they may have simply become habits. Showing and doing create a natural flow of conversation and each step of doing a certain task can remind the participant of other details and create new questions. Other ways of building rapport is are listening and empathizing, being quiet and letting the participant talk, using encouraging probes to trigger more stories and mirroring participants like nodding when they node or smiling when they smile.

Creating interview questions is the most important step of gathering rich data from your interviews. In asking questions descriptive questions are especially useful to start the conversation and keep a participant freely talking. Following are some useful examples for creating descriptive questions.

Descriptive Question Examples

• Could you describe a typical …?
• Could you tell me how you usually make…?
• Could you describe what happened …. from the moment ..until …?
• Could you show me …?
• Could you give me an example of …?
• Could you tell me about some experiences you have…?
• If …, what would you do/say/think?
• Imagine yourself ….what kinds of things/what would you…?

In asking questions, put forward your own idea by asking leading questions. An example of a leading question is, “ Do you follow the latest fashion trends ?” In a subtle way this raises the prospect that maybe you don’t seem like you follow fashion trends.

It is very important to avoid multiple questions in one question and questions where yes/no would be the response. Asking a question like “ do you like simplicity in fashion ?” is not going to reveal much other than yes or no.

Asking questions such as “What do you mean by that?” when something is not clear may contain a hidden judgmental component. Such a question may seem to mean you haven’t been clear; you haven’t adequately explained; you are hiding the true reasons for what you told me (Spadley, 1979). Instead of asking for meaning, it works best to ask for use such as: What are some other ways you could talk about…? Can you think you think of some other examples of…?

During interviews making repeated explanations and restating your goal help to put the conversation in context (Spradley, 1979). As I said earlier, I’m interested in finding out …. I want to understand …. from your point of view

Also during the interview you can select key phrases and terms used by participants and restate them. Restating in this manner reinforces what has been said by way of explanation. Restating demonstrates an interest in learning their language and culture. Restatement must be distinguished from reinterpreting, a process in which the interviewer states in different words what the participant said. Reinterpreting prompts interviewees to translate while restating prompts them to speak in their own ordinary, everyday language (Spradley, 1979).

People tend to summarize their experience by abstracting from a number of concrete experiences when they are asked to talk about them. It is human nature to provide a general impression instead of focusing on all the little details that formed that general impression. Using probes in addition to supportive and encouraging manners is a strategy to overcome abstraction and probe deeper (Chambers,1992). The use of Cultural probes is a technique for gathering inspirational data about the peoples, lives, values and thoughts (Gaver, Dunne & Pacenti, 1999).The probes are small packages that can include any sort of artifact (like a map, postcard, camera or diary) along with evocative tasks, which are given to participants to allow them to record specific events, feelings or interactions. For example you can ask people to keep diaries or record photos that capture a sense of their day or to remember a specific activity like dressing up or shopping; you can ask people to record places they visit for fashion shopping on a map; you can ask people to upload visuals or texts to a social networking site about favorites they own, things they aspire to, technology they use, daily life (shopping, home, work leisure). Then this information is used as a guide during the interviews and can be used to as a referral point to probe deeper. By sharing data this way, the information becomes public, both researcher and user can point out, manipulate and discuss the information.

Focus groups are basically group interviews where overlapping spread of knowledge is gained at once. They are useful to understand some sensitive topics that may be discussed in a group more easily than individually and to eliminate dominance of single voice. However, one very big disadvantage of focus groups is that participants influence each other. Participants may be affected by others’ ideas or they might be too intimidated to answer questions in a group. Focus groups are rarely successful where one person has the power card and is in charge of the whole process. For focus groups to be successful they should be done with mutual relaxed rapport, in an environment where people can freely navigate, share ideas, point out and show things.

During the interview process just like observation you should record data in physical and digital mediums. Note taking should be always supported with voice and/or video recording. Having multiple interviewees in the field can help you focus on the process better as one person can ask questions while another person can take notes and be in charge of video recording. But as the number of interviewees increases it can get more intimidating for participants; therefore it is very important to balance the number.

After the interviews are completed, type up the notes and transcribe the recordings. As a result you might validate your prior findings, develop new insights or discover new veins of research. Such discoveries may require online or face-to-face follow up interviews.

Once you have fully established a clear understating of your topic using interviews and observations then you may want to move onto quantitative research. Using surveys in addition to interviews and observations triangulates research results. Triangulation or cross-examination indicates that more than two methods are used in a study (Beebe, 1995). The idea is that a researcher can be more confident with a result if different methods lead to the same result.

You can gather statistical and numerical data with surveys while observations and interviews are qualitative in nature – promoting understanding of needs, aspirations, and limitations. Interviews and observations are about things that are hard to measure with numbers; surveys are mainly about things you can easily measure using a numerical scale. Surveys are helpful in identifying some general facts about people involved in the trend and for creating typologies. For example, you can gather generalizable data about demographics of the people, ages of the people in relation to adoption of the trends, percentage of male and female members in this target population, the income levels of the people, about their ethnicity, about the regions in which members of this group live, etc. It is also possible to gather data about psychographics by asking them how they value something using a scale. You can also compare participants across demographics, psychographics and geographics. When surveys are repeated over time as longitudinal surveys, it is possible to determine the status of a trend, whether it is at the beginning of an innovation curve or moving from innovators to mainstream.

Using surveys requires careful identification of target populations. Participants who are not part of the target population skew the data when they are surveyed (Raymond, 2011). For example, if you are doing a long-term trend forecasting abut children’s trends, then you probably want to target people who have children and exclude people who do not. How can you do this? You can use a screener or trick question that will help you determine those who are not part of the target population. A screener question is asked as the first question in the survey. It should be asked so that the respondent can’t guess the correct answer in order to proceed to the next question (Raymond,2011). For example, if you are working on active wear trends, you will want to exclude people who do not exercise with a screener question such as:

How many times do you exercise a week?

• I do not exercise
• Once a week
• Twice a week
• Three times a week
• More than three times a week

In this case, a person who answers, “I do not exercise” can be excluded from the sample.

Developing survey questions that will lead to meaningful data requires very careful planning and understanding of the types of questions you can ask and types of answers they can reveal. A trial or pilot test with a small sample can help you determine if you are getting the responses you want.

Demographic Questions Demographic information describes a person. Demographic questions ask about age, gender, occupation, education, family, nationality, income, and such. These types of questions help you compare results across categories. For example, you can compare answers of female and male respondents.

Behavioral Questions These questions are about how a person behaves and about certain actions.

How many pairs of jeans have you purchased in the last three months? Or In the last three months I bought

• one pair of blue jeans
• more than two pairs of blue jeans
• no blue jeans

Attitudinal Questions These questions are about how a person thinks and feels about something rather than what a person does.

On a scale of 1 to 5, how do you feel about simplicity in fashion? 1- Very dissatisfied 2-Dissatisfied 3-Neither satisfied nor dissatisfied Satisfied 4- Satisfied 5-Very satisfied

There are different ways of asking demographic, behavioral and attitudinal questions. You can ask participants to choose from a set of answers, you can ask them to order things, or you can have them use a scale to determine their position on a particular subject.

Multiple Choice The multiple-choice question consists of three or more mutually exclusive answers. These types of questions are widely used for demographic and behavioral questions:

What is the highest degree or level of school you have completed? No schooling completed High school degree College degree Graduate degree

Rank Order Rank order questions allow ranking based upon a specific attribute or characteristic. You can also use images of products and compare products, tastes, aesthetics preferences or mainstream and emerging trends. These questions are especially useful for developing attitudinal survey questions.

Please rank the following brands according to their aesthetic appeal. Place a “1” next to the brand that is most aesthetically pleasing to you and so on. __ GAP __ Abercrombie __ H&M __ Urban outfitters

Rating Scale A rating scale question requires a person to rate something along a well-defined, evenly spaced continuum. Rating scales are often used to measure the direction and intensity of attitudes so it is good for attitudinal survey questions.

Which of the following categories best describes your last experience …? –Very pleasant –Somewhat pleasant –Neither pleasant nor unpleasant –Somewhat unpleasant –Very unpleasant

The Semantic Differential Scale (Likert Scale) You can ask a participant to choose where his or her position lies on a scale between two bipolar adjectives. The semantic differential scale asks a person to rate a trend, product, brand, or company based upon a five-point or seven-point rating scale that has two bi-polar adjectives at each end. Unlike a rating scale it does not have a neutral middle option and a person is asked to evaluate things to an extent.

Would you say simplicity in fashion is: (5) Very Attractive (4) (3) (2) (1) Very Unattractive

Common rules applicable to all types of survey questions are avoiding yes/no questions similar to interviews and developing first response statement answers, which are more emotional and subjective.

There are many organizations and companies as well as governmental agencies that run surveys and build databases. Mori, Mintel, YouGov, Harris Research and Pew Global are a few of them. For these types of firms the number of people who should take the survey depends on the geographic area in which they operate, as it is proportional to the population. For example, 1000 is the minimum standard requirement for most European countries, while in the United States this can rise up to 10,000 in relation to over 300.000 population (Raymond,2011). How many participants should you have when you conduct surveys? The minimum number for statistical significance is 30. With at least 30 participants, you can generalize for those results.

In addition to conducting physical surveys there are free online tools like Surveymonkey.com or limesurvey.org for running online surveys. You can use social media and websites like Facebook to reach out to your target population to conduct your survey.

In analysing survey results, the first step is listing key findings. Survey results can show common threads but can also display anomalies and thus, you need to detect these anomalies. Once all the findings are listed, they should be analyzed in relation to findings from desk research and qualitative research (observation and interview). As stated earlier, one of the benefits of combining desk research and methods of field research is data triangulation. Data triangulation happens when a finding is verifiable with multiple research methods. This adds to your credibility and makes your findings and predictions about future trends stronger.

In addition to desk research and field research, competitive research monitors activities of competing companies with similar consumer bracket and product categories. Competitive research can identify competing companies’ offerings and predict their responses to future trends and market conditions. It offers potential benefits for understanding the market in which a company operates, improved targeting of consumers and finding niche consumer segments It is also highly beneficial in determining pricing strategies.

However, competitive research can sometimes be a dangerous tool as companies may replicate their competitor’s strategies. What is happening in the marketplace today is obvious to everyone and do not create new opportunities. Trend forecasting should be about finding out what’s going to be big and unique tomorrow while competitive research should be used as a tool to differentiate brands and products from those of competing companies in the context of future trends.

Fashion designers and merchandisers shop the marketplace locally and globally to benchmark and collect inspiration from innovative companies. Visiting competition first hand and experiencing competitor’s products and pricing as a consumer provide valuable insight for gaining an understanding of the marketplace and positioning trend forecasting efforts within the competition. Companies can purchase services such as Hoovers and Dun & Bradstreet that can provide detailed competitive analysis. They offer data about history, directors, consumers, employees and recent developments as well as their products and pricing.

Application Exercise

This process is intended to help you learn about identifying emerging mega trends through desk research

• Build a network composed of various interest groups (i.e. architecture, interior design, product design, food, entertainment, cosmetics…)
• Subscribe to newsletters of collaborative trend research/forecast websites (i.e. springwise.com, trendwatching.com…)
• Follow experts, influential people and professionals related to trend forecasting in social media
• Subscribe to personal blogs on trend forecasting
• Research through the online network you have built to detect repeating patterns/ ideas for an emerging trend. Expect your research to be very open at the beginning. It will get more and more focused as you proceed and start seeing patterns. As you research, create a visual and textual library that outlines the emerging trends you have tracked. Start grouping links, images and texts about repeating patterns/ideas.
• What are the common characteristics you have detected across various industries related to the emerging trend?
• Who are experts/knowledgeable people related to it?
• Who are potential target population for this emerging trend?
• Name an emerging trend of your choice and write a concise trend thesis defining its major characteristics.

Beebe, J. (1995). Basic concepts and techniques of rapid appraisal. Human Organization, 54(1), 42-51.

Beyer, H., & Holtzblatt, K. (1998). Contextual design: Defining customer-centered systems. San Francisco: Morgan Kaufmann.

Brown, T. (2009). Change by design: How design thinking transforms organizations and inspires innovation. New York: Harper Business.

Chambers, R. (1992). Rural appraisal: Rapid, relaxed and participatory. Brighton: Institute of Development Studies.

Gaver, W., Dunne, T., Pacenti, E. (1999). Cultural Probes. ACM Interactions. 6(1), 21-29.

Hampden-Turner, C., & Trompenaars, F. (1997). Riding the waves of culture: Understanding diversity in global business. London: N. Brealey.

Pine, B. J., & Gilmore, J. H. (1999). The experience economy: Work is theatre & every business a stage. Boston: Harvard Business School Press.

Raymond, M. (2011). The Trend Forecaster’s Handbook, London: Lauren King.

Rocamora , A. (2013). Personal Fashion Blogs: Screens and Mirrors in Digital Self-portraits, Fashion Theory, 15 (4), 407–424

Spradley, J. P. (1979). The Ethnographic Interview, Holt, Rinehart and Winston‬.

Toffler, A. (1981). The third wave. New York: Bantam Books.

Vanston, J.H., & Vanston, C. (2011), Minitrends: How Innovators & Entrepreneurs Discover & Profit From Business & Technology Trends, Texas: Technology Futures

Communicating Fashion: Trend Research and Forecasting Copyright © by kjensen. All Rights Reserved.

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Title: leveraging multiple relations for fashion trend forecasting based on social media.

Abstract: Fashion trend forecasting is of great research significance in providing useful suggestions for both fashion companies and fashion lovers. Although various studies have been devoted to tackling this challenging task, they only studied limited fashion elements with highly seasonal or simple patterns, which could hardly reveal the real complex fashion trends. Moreover, the mainstream solutions for this task are still statistical-based and solely focus on time-series data modeling, which limit the forecast accuracy. Towards insightful fashion trend forecasting, previous work [1] proposed to analyze more fine-grained fashion elements which can informatively reveal fashion trends. Specifically, it focused on detailed fashion element trend forecasting for specific user groups based on social media data. In addition, it proposed a neural network-based method, namely KERN, to address the problem of fashion trend modeling and forecasting. In this work, to extend the previous work, we propose an improved model named Relation Enhanced Attention Recurrent (REAR) network. Compared to KERN, the REAR model leverages not only the relations among fashion elements but also those among user groups, thus capturing more types of correlations among various fashion trends. To further improve the performance of long-range trend forecasting, the REAR method devises a sliding temporal attention mechanism, which is able to capture temporal patterns on future horizons better. Extensive experiments and more analysis have been conducted on the FIT and GeoStyle datasets to evaluate the performance of REAR. Experimental and analytical results demonstrate the effectiveness of the proposed REAR model in fashion trend forecasting, which also show the improvement of REAR compared to the KERN.

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International Journal of Interdisciplinary Research

• Open access
• Published: 05 March 2022

Analyzing genderless fashion trends of consumers’ perceptions on social media: using unstructured big data analysis through Latent Dirichlet Allocation-based topic modeling

• Hyojung Kim   ORCID: orcid.org/0000-0001-9422-1944 1 ,
• Inho Cho 1 &
• Minjung Park   ORCID: orcid.org/0000-0003-3040-2759 1  

Fashion and Textiles volume  9 , Article number:  6 ( 2022 ) Cite this article

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After the development of Web 2.0 and social networks, analyzing consumers’ responses and opinions in real-time became profoundly important to gain business insights. This study aims to identify consumers’ preferences and perceptions of genderless fashion trends by text-mining, Latent Dirichlet Allocation-based topic modeling, and time-series linear regression analysis. Unstructured text data from consumer-posted sources, such as blogs and online communities, were collected from January 1, 2018 to December 31, 2020. We examined 9722 posts that included the keyword “genderless fashion” with Python 3.7 software. Results showed that consumers were interested in fragrances, fashion, and beauty brands and products. In particular, 18 topics were extracted: 13 were classified as fashion categories and 5 were derived from beauty and fragrance sectors. Examining the genderless fashion trend development among consumers from 2018 to 2020, “perfume and scent” was revealed as the hot topic, whereas “bags,” “all-in-one skin care,” and “set-up suit” were cold topics, declining in popularity among consumers. The findings contribute to contemporary fashion trends and provide in-depth knowledge about consumers’ perceptions using big data analysis methods and offer insights into product development strategies.

Introduction

Consumers’ blogs and social network opinions have become a valuable resource for gaining marketing insights and relationship management (Zhang et al., 2009 ). As social media has profoundly changed our lives, the widespread adoption of social media sources has generated a vast amount of textual data. Knowledge acquired from social networks interacts with consumers and affects many companies to find their competitive advantage in improving brand products or services (Governatori & Iannella, 2011 ; He et al., 2013 ). Consumer-driven fashion trends and continuous social media monitoring has created new paradigms of trend emergence, which lead to the discovery of key values for brands. For example, the traditional runway collections’ design aspects indicated the upcoming fashion trends; however, a social media platform with real-time content posted by consumers, influencers, and brands became streamlined fashion trends (Yotka, 2020 ).

Trend analysis is a technique that attempts to collect information and discover patterns and estimate future predictions (Immerwahr, 2004 ). The fashion industry adapts the trend analysis using the text-mining technique to predict consumer nature, which is associated with business success. The growth of Web 2.0 and social networks has increased the demand for unstructured data such as news, images, and videos online. According to IBM’s report, unstructured data accounted for 93% of the total data in 2020, and it is estimated that 1.7 MB of data are generated every second (Trice, 2015 ). Liu et al. ( 2011 ) found that 80% of an organization’s information consists of text documents, and that using automated computer techniques is essential to exploit the knowledge from the vast amount of text. However, investing consumers’ preferences and adaptation behaviors toward fashion trends is difficult because social media text-based communication analysis is costly and complicated in processing natural language.

The fashion trend implies various societal types and numerous clothing style choices according to different types of societies. Liberal society members tend to be more accepting of radical changes and innovation, while the conservative society community prefers to maintain its conventional costume (Kawamura, 2018 ). South Korea is famous for its highly fashion-conscious consumers who rapidly adjust to emerging trends (Hounslea, 2019 ), as they are willing to engage in digital technology development (Chakravorti et al., 2020 ). At 87%, South Korea’s social media rate is the third highest in the world, enabling consumers to easily follow current widespread trends and to generate new information (Shim, 2020 ). Given that gender fluidity in fashion has seen a recent boom globally since 2018 (Menkes, 2018 ), the genderless concept began to expand as the trend of emphasizing gender diversity expanded in South Korea. Szmydke ( 2015 ) explained that the traditional fashion industry has been providing design and service based on gender identity; however, masculinity and femininity have diversified with the advent of genderless fashion trends. In addition to the importance of the individual’s unique taste on style, current consumers independently define and express their gender identity (Kopf, 2019 ). Clothing is not only a simple method to express one’s lifestyle, but also a strong tool to represent one’s characteristics. Fifty-six percent of Gen-Z consumers who have a spending power of over 140 billion dollars shop outside of their designated gendered area (Marci, 2020 ), and searches for the term “genderless fashion” increased by 52% (Lyst, 2019 ). Moreover, 51% of gender-neutral global fragrance items were launched in 2018 (Murtell, 2019 ), and many fashion brands promoted a campaign of diversity and inclusivity in terms of gender, ethnicity, and body image. The men’s cosmetic market has grown 1.4 times in 5 years—reaching 1.4 trillion KRW (Lee, 2019 ) in South Korea in response to preferences for genderless items.

Therefore, the demand for adapting the genderless fashion trend has risen among general consumers and gender-neutral apparel has strode into retail prominence. Although very few studies have analyzed fashion trends in consumer behavior using the text-mining technique (Blasi et al., 2020 ; Rickman & Cosenza, 2007 ), no previous studies have focused solely on consumers’ perceptions of genderless fashion trends. Moreover, many researchers have explored genderless fashion in terms of design style elements, collection image characteristics, and sociocultural impact (Jordan, 2017 ; Rocha et al., 2005 ; Shin & Koh, 2020 ; Xu & Li, 2012 ) through qualitative research methods. The prominent genderless fashion trend is increasing, and the massive amount of big data has made it possible to understand consumers’ requirements and demands. To the best of our knowledge, this is the first study to evaluate consumers’ awareness of the current genderless fashion trend using the text-mining method. Therefore, this study demonstrates the genderless fashion trend perception among consumers on social media by applying textual data. More specifically, this research aims to answer the following questions.

What major keywords do consumers use when commenting on genderless fashion?

What are the main topics of genderless fashion and how do consumers perceive it?

How have the genderless fashion trends changed over time?

To investigate the research questions, we utilized a probabilistic topic modeling approach known as Latent Dirichlet Allocation (LDA; Blei et al., 2003 ; Griffiths & Steyvers, 2004 ; Newman & Blocks, 2006 ) for consumers’ narrative postings on community sites such as blogs and online communities. LDA-based topic modeling is a supervised machine learning algorithm used to extract latent topics from the thematic structure of large volumes of texts (Elgesem et al., 2015 ). The computational content-analysis of LDA-based topics enables the classification of large amounts of unstructured text documents. Consequently, LDA-based topic models are efficient in discovering and describing hidden semantic structures in a collection of texts (Koltsova & Shcherbak, 2015 ). In particular, we analyzed the search keyword “genderless fashion” on the portal site NAVER ( http://www.naver.com )—the largest web search engine in South Korea. We then examined consumers’ perceptions of genderless fashion over the past 3 years by collecting their texts from blogs and online communities. After the data cleansing and preprocessing procedures, we specified the top keywords to extract the topics. Then, an n-gram analysis (Wallach, 2006 ) was applied to categorize the continuous sequence of high-order phrases from the morphologically analyzed texts. To define the number of topics, perplexity and coherence tests were examined for interpretability verification. The intertopic distance map (IDM; Blei et al., 2003 ) was used to determine the similarity of the chosen topics using a graphic plot showing the specific gravity of the topic and the distance among the topics. Finally, the selected topics were labeled and compared with the representative documents, and a time-series analysis was performed to measure the topic trend change.

This study advances our in-depth understanding of genderless fashion trends and contains diverse perspectives on consumers’ behaviors and interests. This study explains how fashion trends are perceived and commercialized, related to consumers’ use of social media. Further, we extend our research on fashion trend analysis by applying text-mining algorithms to extract the most relevant topics, which goes beyond the findings in the existing literature. Despite the high level of demand among consumers in the pursuit of acceptance of various gender identities in the fashion industry, relevant studies are scarce. In this context, research on genderless fashion trend analysis based on a consumer-driven text-mining analysis is essential, and the current findings will enable fashion brands to forecast customers’ preferences for purchasing gender-neutral products and develop marketing strategies through social media channels.

Literature review

• Genderless fashion trend

The genderless fashion phenomenon has recently emerged as a new standard and has been cited as a major trend among consumers (Bernard, 2018 ; Kerpen, 2019 ; Segalov, 2020 ). The term “genderless” is also referred to as “agender,” “gender fluidity,” “gender neutral,” “gender diversity,” and “gender-free”—all of which refer to the state of being without a clear gender identity (Robinson, 2019 ). It refers to using products and creating styles according to individual personality and taste from a neutral perspective, regardless of gender. Most societies define traits specific to a gender and orient their members in that direction (Risman & Davis, 2013 ); however, genderless is interpreted as a movement to remove the social division between women and men and regard them as neutral individuals. For example, the binarity of gender was classified into distinct male and female segmentations, producing various stereotypes and corresponding behaviors. Strict adherence to traits of masculinity and femininity were expected from each sex, and costumes reflected the resulting dichotomous social norms. The perception of gender was influenced by factors such as feminism and relevant social movements in the 1960s and the development of mass media and the change from biological sortation to social gender. This had an impact on “androgynous” styles in the 1970s and “glam” looks in the 1980s, which transformed into the “unisex” concept, described as suitable for both males and females (Bardey et al., 2020 ; Mills, 2015 ). Lee ( 2021 ) highlighted that unisex is different from genderless fashion in terms of distinguishing methods to differentiate gender; it is based on the gender distinction between men and women, and embraces the same design, whereas the genderless style does not dichotomize gender and encompasses a wide spectrum of gender identities.

Millennials and Generation Z have different values and lifestyles than the previous generations, particularly in relation to the traditional gender role distinction. As the leading groups of trends and consumption, they want to define and express their gender identity on their own because of their great desire to express their social influence and external images (Wertz, 2018 ). A recent survey indicated that 38% of Generation Z and 27% of Millennials, who will account for $143 billion purchasing power in the next 4 years (Anyanwu, 2020 ), agreed that an individual cannot be judged or determined by gender. With this in mind, high-end brands projected runway models indistinguishable in terms of gender, while masstige brands introduced retail strategies to eliminate the distinction between men’s and women’s products in stores or launched new public brands. In addition to women’s and men’s wear brands, one major children’s wear brand removed boys’ and girls’ labels from the store floor plan to reinforce the extensive product choice preferences (Newbold, 2017 ), eliminating gender stereotypes for their customers.

Text-mining analysis

Text-mining is an artificial intelligence technology that utilizes natural language processing to obtain meaningful information from vast unstructured textual data (Liu et al., 2011 ; Nishanth et al., 2012 ) or to estimate uncertain patterns (He et al., 2013 ). It includes the processes of editing and organizing several documents composed of words, characters, and terms (Nishanth et al., 2012 ). As a big data analytics extension technique, text-mining analysis examines large and varied data documents to uncover nontrivial information such as unknown correlations, customer preferences, and market trends that aid in the best decision making in the business (Hashimi et al., 2015 ). In particular, after the rapid increase in social network services, social media mining has been adopted to understand and interact with customers and gain a competitive advantage. According to Reports and Data ( 2020 ), the text-mining market will reach $16.85 billion by 2027 owing to the high rise in the adoption of social media platforms, and many business organizations have deployed text-mining analytics to transform data into competitive knowledge.

Many previous researchers have used text-mining techniques to analyze consumers’ brand sentiments (Mostafa, 2013 ), to measure consumer preferences (Rahman et al., 2014 ), and to survey the commerce trend on social media (Shen et al., 2019 ). Regarding fashion, Lang et al. ( 2020 ) evaluated consumers’ fashion-renting experiences through in-depth text analysis using LDA-based topic modeling, and Dang et al. ( 2016 ) classified fashion content texts from social networks using a support vector machine. Choi and Lee ( 2020 ) researched ethical fashion using text-mining with network analysis, and Lee et al. ( 2018 ) analyzed luxury fashion brands and mass brands’ evaluations of Twitter messages. Owing to the strong capabilities of text-mining techniques, many attempts have been made to analyze social media content to yield valuable findings on consumers’ behavior and sentimental values toward a brand. However, previous studies have dealt with relatively limited information, focusing solely on consumers’ perceptions of genderless fashion trends. Consequently, to analyze mainstream fashion trends and understand consumers’ interests, a text-mining method was employed for this study.

LDA-based topic modeling

In this study, LDA-based topic modeling (Blei et al., 2003 ) was utilized to extract customers’ perceptions of the genderless fashion trend on social media. Topic modeling allows the user to detect and summarize latent semantic structures, and LDA is the most common method for clustering abstract topics that occur in a collection of documents (Nabli et al., 2018 ). LDA assumes that documents consist of a mixture of topics, and that topics generate words based on probability distributions. As shown in Fig.  1 , Blei ( 2012 ) explained the LDA model algorithm as follows: the square boxes are called “plates” and “N” stands for a collection of words collected within a document, “D” for a collection of documents, and “K” for a set of topics. The circles represent probability parameters, and the node “ \({W}_{d,n}\) ” is observed as a word in the document; while topics, topic distributions, and topic assignments are not revealed. There are full words (“ \({W}_{d,n}\) ”) in the numerous documents (“D”) collected by the researchers, assuming that each word has a corresponding topic (“ \({Z}_{d,n}\) ”).

Graphics of document generation for LDA algorithm (Blei, 2012 , p. 81)

There are many different topics embedded in each document, and the distribution of topics differs. Therefore, LDA deduces the latent variables of the document through the words contained in the document and generates a specified number of topics from the document stack through the Dirichlet distribution. In this study, LDA-based topic modeling was adopted to understand the consumer-driven content of genderless trends in social media networks. Various researchers have explored LDA-based topic modeling to discover new knowledge about consumers’ communication. Bastani et al. ( 2019 ) analyzed the customer complaints of national financial agencies, and fashion design participants were analyzed to observe research trends (Jang & Kim, 2017 ). Gray et al. ( 2015 ) developed an LDA-based text-mining methodology to define fashion styles obtained from online apparel information with affiliate networks. In contrast to the approach of consumers’ research in the fashion industry conducted in the various studies discussed above, genderless fashion trend research is unknown. Therefore, we developed a primary approach to discover consumers’ preferences and interest in social media toward the genderless fashion trend with an LDA-based topic modeling proposal.

Data collection

We obtained data from the largest Korean search portal engine—NAVER—focusing on consumers’ online community and blog reviews for 3 years since the genderless fashion trend began (Menkes, 2018 ): from January 1, 2018 to December 31, 2020. To gain insights related to genderless fashion trends among consumers’ posts and communication, a search of the keyword “genderless fashion” was conducted, which produced 9722 posts. The web crawling program language Python 3.7 ( http://www.python.org ) was used to build the model. Consumers’ posting date, platform type, title, contents, and link information were gathered; the text-mining objects were title and content. Data were pre-processed to cleanse them of undesirable words, special characters, non-Korean words, and punctuation. Afterward, word tokenization was lemmatized and converted into the minimal unit of meaning formats such as nouns, adjectives, verbs, or adverbs in their dictionary forms. These words were accumulated in the bag-of-words model (Zhang et al., 2010 ), which represents a multiset of words regardless of word order. Words that occurred in 80% of the documents and in fewer than five documents were removed (Jauhari et al., 2020 ). Moreover, search keywords’ implied synonym words such as gender-neutral, gender fluid, gender diversity, and fashion that could have affected the results, were removed. Hence, only meaningful words relevant to the generation of the topics remained.

Measurement and research process

To perform the research data analysis, we used Python 3.7 to perform data processing and applied LDA-based topic modeling. The detailed research process flowchart, performed over four steps, is shown in Fig.  2 . First, the web crawling technique was performed using the keywords “genderless fashion” to collect consumers’ review posts on NAVER’s blogs and online communities. The number of keyword changes over 3 years was evaluated to estimate consumers’ preferences and interests. Second, data cleansing and preprocessing of unstructured text data were conducted to eliminate irrelevant or generic words. Consequently, the entire text document was into split into individual words, which is known as word tokenization. Then, stop-word removal and word lemmatization were applied to filter meaningful words on natural language data. For example, onomatopoeic words (“haha,” “nope,” etc.), emoticons, propositions (“the,” “a,” etc.), inappropriate words (“recently,” “more,” “really,” etc.) were removed. Then, the top 50 text frequency words and bigram rates were analyzed. Next, to analyze topic modeling based on the LDA algorithm, a topic model number was defined by applying the measure of perplexity and coherence parameters. Then, each topic model’s labeling was selected based on the observed keywords and representative documents associated with the high weight of the topic. In this step, an IDM was applied to determine the degree to which each topic was related to other topics and the degree of similarity between topics. Fourth, to measure the topic trend change over the past 3 years, we investigated the number of consumers’ posts containing each topic. Subsequently, a time-series linear regression analysis was performed to confirm the annual trends of the topic.

Research data processing flowchart

Status of consumers’ posts and media news about genderless fashion

We compared the number of posts over 3 years from 2018—when genderless fashion was cited as a major trend—to 2020 by crawling consumers’ blog and online community posts on NAVER and media news posts. In these 3 years, 9722 pieces of consumer-generated content about genderless fashion were uploaded, and the yearly trend showed that the number of online posts had steadily increased: 1435 postings in 2018, 2538 postings in 2019, and 5749 postings in 2020. Consistently, there were 104 online news articles in 2018, 524 in 2019, and 1008 in 2020. As shown in Fig.  3 , both consumers’ and media news outlets’ posts continued to increase, especially in 2020, when it doubled compared to 2019. Therefore, it was confirmed that consumers’ interest in genderless fashion has grown rapidly.

The number of consumers’ posts and media news for 3 years (2018–2020)

Text frequency

To analyze the key terms related to genderless fashion, we combined the titles and contents of consumers’ posts. Data cleansing and preprocessing were essential for generating meaningful topic modeling. We performed word tokenization to analyze the text dataset as a morpheme, turning it into the smallest unit of meaning through natural language processing (Bastani et al., 2019 ). To filter out unnecessary words, stop-word removal (Nabli et al., 2018 ) was conducted, eliminating undesirable fragments such as punctuation, single-letter words, grammatical errors, and numbers. The resulting set was extracted with only nouns and adjectives after word lemmatization, maintaining the basic dictionary form of a word after removing the inflectional endings. Accordingly, the frequency value of the occurrence of all extracted words was obtained, except for the words that appeared more than 80% of the time or in less than five documents. The top 45 keywords based on the extracted frequency are listed in Table 1 . The results of visualizing the top 50 of the highest frequency keywords from 4051 word lists is shown in Fig.  4 . Words with a high frequency of occurrence are expressed as bigger and bolder in the word cloud. To review the top-ranking frequency occurrence words, genderless fashion-related brands (e.g., Gucci, Olive Young) and merchandise (e.g., product, design, style, item, bag, pants, shirts, store, jacket, sunglasses, knit) were extracted in the fashion and beauty industry (e.g., clothes, cosmetics, hair, makeup, jewelry). Concerning color, black was the highest, followed by white, blue, gray, and green (in order).

Word cloud visualization results

N-gram analysis

We attempted to improve text classification by determining which words were connected in the unigram dataset. Bigram means that two-word phrases belong to the n-gram analysis method to generate contiguous word pairs in the corpus and gain the contextual word association (Crossley & Louwerse, 2007 ). It is also useful to compare bigrams in two different sentences because it allows us to identify the similarities and various types of words in context. The results of the top 35 bigrams from 6703 two-word lists are shown in Fig.  5 . Cosmetics (e.g., super hyalon, skincare, mask pack, moisture line, hand cream, basic cosmetics, skin moisturizer, BB cream), fragrance (e.g., perfume recommendation, body spray, Eau de perfume), fashion brands (Maison Martin Margiela, Thom Browne, Zadig & Voltaire, Push the Button, Bottega Veneta), and style-related items (oversize, wide pants, jogger pants, denim pants, wild slacks) appeared accordingly.

Results of the bigram analysis

Select the optimized number of topics

We analyzed the coherence score and perplexity score to evaluate the optimal number of topics as quantitative diagnostic metrics. The coherence score measures how frequently the top keywords of each topic co-occur to identify which of the top words contributes the most relevant information to the given topic (Blair et al., 2020 ). The perplexity score is an indicator of whether the topics are clearly classified, and it is assumed that the smaller the value, the better the actual literature results reflected by that topic (Inglis & Foster, 2018 ). Therefore, the smaller the perplexity value and the larger the coherence value, the more semantically consistent the topic model that is constructed. By calculating the perplexity and coherence values for all the words in the web-crawled document, we ensured that the LDA-based model achieved maximum coherence score and minimum perplexity score with the number of topics ( k  = 18; Fig.  6 ).

The interpretability of topic modeling

Topic selection and labeling

The IDM for topics extracted from topic modeling in this study is shown in Fig.  7 . IDM is a diagram that shows the weight of a topic and the distance between topics, and makes it possible to understand the degree of relevance of each topic to other topics (Sievert & Shirley, 2014 ). The topic view is on the left, and the term bar charts with 18 topics selected are on the right. Selections are linked so that the researcher can briefly demonstrate the aspects of the relationship of the topic terms. The distribution of topics related to the subject shows that the proportion of each topic is similar, which confirms that the deviation is non-significant. Furthermore, because the topics do not altogether overlap with each other, the association between the topics is low, which means that each topic is divided into a relatively clear research area.

Intertopic distance map (IDM) of genderless fashion LDA topic modeling

We classified consumers’ perceptions of genderless fashion for 3 years by identifying keywords derived using LDA-based topic modeling algorithms and documents with a high weight of the topic. Table 2 shows the topic number corresponding to the keywords of the topic, the weight of the topic in the document, the date of the posting, and the title as an example from topic number one to five.

The topic labeling process was discussed and confirmed by five experts in the fashion and textile industries. Table 3 represents 18 topics and top keywords for genderless fashion trend topic modeling in accordance with the analysis of major keywords and documents with high topic weight in previous works.

Time-series analysis

To understand the trend of each topic by year, the year was applied as the independent variable, the weighted average value of the topic by year was used as the dependent variable, and a series linear regression analysis was performed. In addition, the values of the regression coefficient and the significance probability of the linear regression analysis were verified as criteria for judging the rise and fall of trends by year. Only those topics with a significant p-value (< 0.05) and a Durbin–Watson value greater than 1.5 and less than 2.5, if the regression coefficient value was positive, were classified as a “hot topic”; while, if negative, they were classified as a “cold topic,” and topics for which no meaningful result could be derived were classified as a “neutral topic” (Griffiths & Steyvers, 2004 ).

The hot topic that has been rapidly growing among consumers in the genderless fashion trend for the last 3 years was “perfume and scent.” In contrast, “bags,” “all-in-one skin care,” and “set-up suit” (i.e., a casual outfit that can be used together or worn separately with a jacket and pants) were cold topics, indicating that consumers’ interest in these gradually declined. The remaining topics were classified as neutral topics because they were non-significant in the time-series analysis (see Table 4 ).

The concept of gender diversity has begun to expand with the trend of focusing on individuals’ unique taste importance. Consumers began to self-define and express their gender identity and discuss it through social media channels. With access to massive amounts of unstructured data from blog and online community reviews, the purpose of this study was to identify consumers’ perceptions and preferences regarding genderless fashion based on the text-mining analysis approach. In particular, we selected the LDA-based topic modeling method to examine a large amount of qualitative information obtained from consumers’ posts.

Text data were collected from the search keywords “genderless fashion” on the NAVER portal site from January 1, 2018 to December 31, 2020. A total of 9722 postings were collected, word tokenization was conducted after data preprocessing and cleansing, and word frequency and n-gram analysis were performed to remove stop words. To determine the optimal number of topics, perplexity and coherence scores were evaluated, and 18 topic keywords were finally selected through the LDA algorithm analysis. To select each topic, the contents of the representative documents with a high weight of the topic were reviewed. Finally, a time-series regression analysis was performed to understand the trend of topics by year, and the hot topic of the uptrend and the cold topics of the downtrend were selected.

First, to review the text frequency and n-gram analysis results, our study findings revealed that consumers are interested in external images as independent individuals rather than meeting other people’s standards, and often talk about fashion brands (“Gucci,” “Maison Martin Margiela,” “KIVULI,” “Zadig & Voltaire,” “Push the Button,” “Bottega Veneta”), items (“clothes,” “bag,” “pants,” “shirts,” “jacket,” “sunglasses,” “jewelry,” “style”), and cosmetic and perfume products (“skin,” “sheet mask,” “skin moisturizer,” “makeup,” “body spray,” “body mist,” “hand cream,” “Eau de perfume,” “Super hyalon,” “BB cream,” etc.) related to genderless fashion. This indicates that consumers’ recommended products and styles of genderless fashion are affected by the diverse fashion labels collection. Consumers are interested in the coordination and design details of certain brand items related to the gender-neutral concept. In particular, beauty cosmetics and fragrances, which are dominated by female-oriented stereotypes, are now being highlighted, regardless of gender division, owing to the influence of genderless fashion trends among consumers. Kim ( 2021 ) stated that many brands are launching gender-free cosmetics, which has become an opportunity for male consumers’ interest in skin care to become specialized. These results can be understood in the same context as those of previous studies (Newbold, 2017 ; Reis et al., 2018 )—that genderless fashion is a response to the needs of the fluid market niche increase aside from femininity and masculinity stereotypes. Wertz ( 2018 ) also indicated that Millennials and Generation Z’s consumption trends value individuality and practicality rather than gender. Concerning color, An ( 2018 ) as well as Hong and Joo ( 2020 ) mainly pointed out that “pink” was the trendy color on gender-neutral menswear collections; however, we discovered that achromatic colors such as “black,” “white,” “gray” mentioned mostly among the consumers. The results suggested that the men’s collection combines colorful colors into genderless fashion, but our study confirmed that consumers prefer dark colors.

Second, 18 topics were analyzed from LDA-based algorithms and 13 topics were classified as fashion categories (i.e., “summer jewelry,” “men’s fashion & grooming,” “hairstyle & color,” “high-end fashion’s basic item,” “bags,” “FW fashion,” “collaboration,” “genderless concept models,” “luxury brand sunglasses,” “pants style,” “set-up suit,” “domestic eyewear brand,” and “capsule collection”), while 5 topics were classified as beauty and fragrance categories (i.e., “moisturizing skin care,” “perfume and scent,” “cosmetic beauty brands,” “body spray,” and “all-in-one skin care”). The fashion industry has provided designs and services differently according to gender (Szmydke, 2015 ); however, new product development and rebranding strategies have emerged in accordance with the gender fluidity change followed by consumer-driven change. Previous studies (Hong & Joo, 2020 ; Shin & Koh, 2020 ) have investigated genderless fashion in terms of design and style based on the collection images. Kim ( 2020 ) and Yang ( 2020 ) researched genderless trends in cosmetic brands’ advertisements. Hence, our results indicated that the main interest in the genderless concept of current consumers lies in the fashion and beauty fields by expanding existing qualitative studies using big data. In particular, South Korea’s male cosmetics consumption is number one in the global market (Im, 2016 ), which is consistent with our results. The effect of the gender fluidity phenomenon on the beauty industry was also revealed in our results (e.g., “super hylaon,” “LAKA”) as the genderless-only cosmetic brands.

Third, our time-series linear regression analysis revealed a hot topic (“perfume and scent”) and three cold topics (“bags,” “all-in-one skin care,” and “set-up suit”), while the rest were presented as neutral topics. The topic that has continuously grown among consumers in relation to the genderless fashion trend in the last 3 years has been “perfume and scent.” Certain brands of seasonal perfumes (“Jo Malone,” “Diptyque”) and scents (“Eau de perfume,” “common”) were mentioned among the consumers. As “gift” suggests in the topic, genderless fragrances have a sensuous and soft scent, which are easy to give as a present regardless of gender. According to BBC’s report, gender fluid fragrances have surged in popularity, increasing to 51% as compared to 17% in 2010 (Bolongaro, 2019 ). In contrast, “all-in-one cosmetics” attracted high consumer interest in the beginning, but they gradually declined in popularity. The high demand for “moisturizing skin care” indicates that male consumers used all-in-one products because of their convenient usage in the past; however, now they can choose exclusive genderless products, allowing them to choose their own products by function and purpose (Hong, 2020 ).

Considering the changes in consumers’ perception of fashion products, interest in bags has been declining. “Tote bags” and “size” were considered because of users’ light-weight concerns, and they referred to the brand look-book (“Beanpole”) or fashion week collection (“Juun. J,” “Push the Button”). Yoo ( 2020 ) explained that handbag brands have expanded the range of tote bags, particularly because of their unique characteristics as well as the effect of genderless fashion trends. The “set-up suit” topic also showed a steady decline. Business casual suits are tailored (“custom”) or users prefer practical styling with a comfortable pattern (“comfort”) along with the demand for female consumers. Demand for women’s suits increased with the growth of genderless fashion, but it seems that the demand has decreased owing to the recent increase in telecommuting under the influence of the COVID-19 pandemic.

Conclusions

Existing research on genderless fashion trends has focused on the style characteristics shown in collections and advertisements (Hong & Joo, 2020 ; Kim & Lee, 2016 ; Yang, 2020 ). Therefore, there is a possibility that our subjectivity was involved and consumers’ perspectives were not included. Recently, the number of consumer-led products and brands has increased remarkably; therefore, consumers’ recognition of fashion trends is critical as they affect the industry enormously. A few studies have focused on consumer reviews on fashion subjects using the big data analysis method. Lang et al. ( 2020 ) investigated consumers’ fashion rental experiences, and Choi and Lee ( 2020 ) studied ethical fashion perception. However, this is one of the first studies that deals with consumers’ preferences for genderless fashion trends by applying text-mining and LDA-based topic modeling techniques. Through this computer-aid method, researchers can extract hidden implications or estimate patterns from a natural language dataset (Hashimi et al., 2015 ). To analyze and understand consumers’ behaviors in real-time is becoming essential; thus, we investigated consumers’ unstructured data in fashion trends analysis.

This study has managerial implications for product planners who develop merchandise based on recent trends. We found that consumers have a high interest in brands and products related to perfume, fashion, and cosmetics in terms of genderless fashion trends that can make their individuality stand out despite gender division. Therefore, when a product planner plans a merchandising product group targeting consumers, these product categories can be prioritized. In particular, given that the topic of “perfume and scent” has been on the rise among consumers, strategic promotions and collaboration with genderless fragrance brands can also be conceived.

The limitations of this study and suggestions for future research are as follows.

This study collected the text documents from consumers postings of blogs and online communities, therefore it is not focused solely on a specific generation. Because the genderless fashion is popularly accepted by Millennials and Generation Z (Anyanwu, 2020 ), it would be meaningful to closely consider the opinions of various generations in the future. Continuous research is expected to be conducted in the field of fashion and textiles, because text-mining research is still scarce and at its nascency. For future research projects, it is necessary to analyze not only consumer opinions related to genderless fashion trends, but also related articles introduced in the mass media. If in-depth analyses can be conducted in the aspects of social interest and the business industry, more insights can be gained to enhance the proposed model in this study.

Availability of data and materials

The datasets used and analyzed during the current study are available from the first author on reasonable request.

Abbreviations

Spring summer

Fall winter

An, K. S. (2018). A study on the characteristic of genderless expressed in modern man’s fashion collection. Journal of Humanities and Social Science, 9 (4), 1543–1556. https://doi.org/10.22143/HSS21.9.4.107

Article   Google Scholar  

Anyanwu, O. (2020, January 8). Decoding genderless fashion, the future of the industry. WWD. Retrieved April 8, 2021, from https://wwd.com/fashion-news/fashion-features/genderless-fashion-future-of-industry-1203381685/

Bardey, A., Achumba-Wöllenstein, J., & Chiu, P. (2020). Exploring the third gender in fashion: From consumers’ behavior to designers’ approach towards unisex clothing. Fashion Practice, 12 (3), 421–439. https://doi.org/10.1080/17569370.2020.1816317

Bastani, K., Namavari, H., & Shaffer, J. (2019). Latent Dirichlet allocation (LDA) for topic modeling of the CFPB consumer complaints. Expert Systems with Applications, 127 , 256–271. https://doi.org/10.1016/j.eswa.2019.03.001

Bernard, K. (2018, July 11). Breaking the binary. The New York Times. Retrieved January 8, 2021, from https://www.nytimes.com/2018/07/11/style/phluid-project-nonbinary-fashion.html

Blair, S. J., Bi, Y., & Mulvenna, M. D. (2020). Aggregated topic models for increasing social media topic coherence. Applied Intelligence, 50 (1), 138–156. https://doi.org/10.1007/s10489-019-01438-z

Blasi, S., Brigato, L., & Sedita, S. R. (2020). Eco-friendliness and fashion perceptual attributes of fashion brands: An analysis of consumers’ perceptions based on twitter data mining. Journal of Cleaner Production, 244 , 118701. https://doi.org/10.1016/j.jclepro.2019.118701

Blei, D. M. (2012). Probabilistic topic models. Communications of the ACM, 55 (4), 77–84. https://doi.org/10.1145/2133806.2133826

Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of machine Learning research, 3 (Jan), 993–1022. https://doi.org/10.1162/jmlr.2003.3.4.-5.993

Bolongaro, K. (2019, December 12). How millennials are changing the perfume business . BBC News. Retrieved April 8, 2021, from https://www.bbc.com/news/business-50585558

Chakravorti, B., Bhalla, A., & Chaturvedi, R. S. (2020, December 18). Which economies showed the most digital progress in 2020? Harvard Business Review. Retrieved January 4, 2021, from https://hbr.org/2020/12/which-economies-showed-the-most-digital-progress-in-2020

Choi, Y. H., & Lee, K. H. (2020). Ethical fashion research trend using text mining: Network analysis of the published literature 2009–2019. Fashion & Textile Research Journal, 22 (2), 181–191. https://doi.org/10.5805/SFTI.2020.22.2.181

Crossley, S. A., & Louwerse, M. M. (2007). Multi-dimensional register classification using bigrams. International Journal of Corpus Linguistics, 12 (4), 453–478. https://doi.org/10.1075/ijcl.12.4.02cro

Dang, N. C., De la Prieta, F., Corchado, J. M., & Moreno, M. N. (2016, June). Framework for retrieving relevant contents related to fashion from online social network data. In  International Conference on Practical Applications of Agents and Multi-Agent Systems  (pp. 335–347). Springer, Cham. https://doi.org/10.1007/978-3-319-40159-1_28

Elgesem, D., Steskal, L., & Diakopoulos, N. (2015). Structure and content of the discourse on climate change in the blogosphere: The big picture. Environmental Communication, 9 (2), 169–188. https://doi.org/10.4324/9781315403625-3

Governatori, G., & Iannella, R. (2011). A modelling and reasoning framework for social networks policies. Enterprise Information Systems, 5 (1), 145–167. https://doi.org/10.1080/17517575.2010.513014

Gray, C., Beattie, M., Belay, H., Hill, S., & Lerch, N. (2015, April). Personalized online search for fashion products. In  2015 Systems and Information Engineering Design Symposium  (pp. 91–96). IEEE. https://doi.org/10.1109/SIEDS.2015.7117018 .

Griffiths, T. L., & Steyvers, M. (2004). Finding scientific topics. Proceedings of the National Academy of Sciences, 101 (suppl 1), 5228–5235. https://doi.org/10.1073/pnas.0307752101

Article   CAS   Google Scholar  

Hashimi, H., Hafez, A., & Mathkour, H. (2015). Selection criteria for text mining approaches. Computers in Human Behavior, 51 , 729–733. https://doi.org/10.1016/j.chb.2014.10.062

He, W., Zha, S., & Li, L. (2013). Social media competitive analysis and text mining: A case study in the pizza industry. International Journal of Information Management, 33 (3), 464–472. https://doi.org/10.1016/j.ijinfomgt.2013.01.001

Hong, Y. D. (2020 July, 26). A man takes care of his appearance...More men looking for beauty items . Yonhapnews. Retrieved April 10, 2021, from https://www.yna.co.kr/view/AKR20200724128600030

Hong, Y., & Joo, M. Y. (2020). A study on characteristics of pink color and fashion images in gender neutral men’s fashion. Journal of Fashion Business., 24 (5), 52–71. https://doi.org/10.12940/jfb.2020.24.5.52

Hounslea, T. (2019, June 27). How to succeed in trendsetting South Korea . Drapers. Retrieved January 4, 2021 from, https://www.drapersonline.com/insight/analysis/how-to-succeed-in-trendsetting-south-korea

Im, H. S. (2016, March 1). World’s no. 1 Korean men’s cosmetics consumption…catch the grooming people . The Asia Business Daily. Retrieved April 9, 2020, from https://www.asiae.co.kr/article/2016030110334497664

Immerwahr, J. (2004). Public attitudes on higher education: A trend analysis, 1993 to 2003. National Center Report Number 04–2.  Public Agenda .

Inglis, M., & Foster, C. (2018). Five decades of mathematics education research. Journal for Research in Mathematics Education, 49 (4), 462–500. https://doi.org/10.5951/jresematheduc.49.4.0462

Jang, N., & Kim, M. J. (2017). Research trend analysis in fashion design studies in Korea using topic modeling. Journal of Digital Convergence, 15 (6), 415–423. https://doi.org/10.14400/JDC.2017.15.6.415

Jauhari, T. M., Kim, S., Kovacs, M., Serdült, U., & Kryssanov, V. V. (2020, March). Assessing customer needs based on online reviews: A topic modeling approach. In CEUR Workshop Proceedings (pp. 57–62). https://doi.org/10.5167/uzh-188603

Jordan, P. (2017). Gender fluidity in men’s fashion: From Shakespeare’s modern English to the new millennium. Critical Studies in Men’s Fashion, 4 (2), 171–184. https://doi.org/10.1386/csmf.4.2.171_1

Kawamura, Y. (2018). Fashion-ology: An introduction to fashion studies . Bloomsbury Publishing.

Book   Google Scholar  

Kerpen, C. (July 8, 2019). It’s time to redefine masculinity . Forbes. Retrieved January 8, 2021, from https://www.forbes.com/sites/carriekerpen/2019/07/08/its-time-to-redefine-masculinity/?sh=77f5895f6315

Kim, B. N. (October 4, 2020). More and more women are looking for #women’s #suits . The Herald Business. Retrieved April 10, 2021, from http://biz.heraldcorp.com/view.php?ud=20200929001046

Kim, M. B. (February 16, 2021). “The barriers to the beauty industry break down” CJ Olive Young pays attention to the “gapless” phenomenon of the MZ generation . DongA.com. Retrieved April 8, 2021, from https://www.donga.com/news/Culture/article/all/20210216/105446960/1

Kim, J., & Lee, Y. (2016). Expressive characteristics of genderless style appeared in contemporary women’s fashion. The Research Journal of the Costume Culture., 24 (6), 903–919. https://doi.org/10.7741/rjcc.2016.24.6.903

Koltsova, O., & Shcherbak, A. (2015). ‘LiveJournal Libra!’: The political blogosphere and voting preferences in Russia in 2011–2012. New Media & Society, 17 (10), 1715–1732. https://doi.org/10.1177/1461444814531875

Kopf, D. (2019, January 20). Generation Z’s views on gender are what set it apart in the US . Quartz. Retrieved January 5, 2021, from https://qz.com/1528752/generation-zs-views-on-gender-set-it-apart-in-us/

Lang, C., Li, M., & Zhao, L. (2020). Understanding consumers’ online fashion renting experiences: A text-mining approach. Sustainable Production and Consumption, 21 , 132–144. https://doi.org/10.1016/j.spc.2019.12.003

Lee, J. H. (2019, December 12). The cosmetics industry focuses on the genderless beauty market . Maeil Business News Korea. Retrieved March 1, 2021, from https://www.mk.co.kr/news/economy/view/2019/12/1072238/

Lee, J. M. (2021, March 11) The ‘gender-neutral’ craze... the beauty industry breaks down the ‘gender barrier’ . Economic Review. Retrieved March 21, 2021, from https://www.econovill.com/news/articleView.html?idxno=522873 .

Lee, S. H. N., Zhou, Y., Lo, C. K., & Ha-Brookshire, J. (2018). Luxury fashion brands versus mass fashion brands: Data mining analysis of social media responses toward corporate sustainability. In  Sustainability in luxury fashion business  (pp. 51–73). Springer, Singapore. https://doi.org/10.1007/978-981-10-8878-0_4 .

Liu, B., Cao, S. G., & He, W. (2011). Distributed data mining for E-business. Information Technology and Management, 12 (2), 67–79. https://doi.org/10.1007/s10799-011-0091-8

Lyst. (2019). The year in fashion . Lyst. Retrieved January 5, 2021, from https://www.lyst.com/year-in-fashion-2019/

Marci, K. (2020, July 23). The need for gender fluidity in fashion beyond pride . Edited. Retrieved January 5, 2021, from https://edited.com/resources/the-need-for-gender-fluidity-in-fashion-beyond-pride/

Menkes, S. (2018, January 14). Gender neutrality becomes fashion reality . Vogue UK, Retrieved January 4, 2021, from https://www.vogue.co.uk/article/gender-neutrality-becomes-fashion-reality

Mills, R. (2015). ‘Transformer’: David Bowie’s rejection of 1960s counterculture fashion through his glam reinvention and stylings in the years 1969–1972. Clothing Cultures, 2 (2), 179–192. https://doi.org/10.1386/cc.2.2.179_1

Mostafa, M. M. (2013). More than words: Social networks’ text mining for consumer brand sentiments. Expert Systems with Applications, 40 (10), 4241–4251. https://doi.org/10.1016/j.eswa.2013.01.019

Murtell, J. (2019, September 12). The rise of gender-neutral branding . Packaging Strategies. Retrieved January 8, 2021, from https://www.packagingstrategies.com/articles/95077-the-rise-of-gender-neutral-branding

Nabli, H., Djemaa, R. B., & Amor, I. A. B. (2018). Efficient cloud service discovery approach based on LDA topic modeling. Journal of Systems and Software, 146 , 233–248. https://doi.org/10.1016/j.jss.2018.09.069

Newbold, A. (2017, September 4). Childrenswear goes genderless at John Lewis . Vogue UK. Retrieved January 8, 2021, from https://edited.com/resources/the-need-for-gender-fluidity-in-fashion-beyond-pride/

Newman, D. J., & Block, S. (2006). Probabilistic topic decomposition of an eighteenth-century American newspaper. Journal of the American Society for Information Science and Technology, 57 (6), 753–767. https://doi.org/10.1002/asi.20342

Nishanth, K. J., Ravi, V., Ankaiah, N., & Bose, I. (2012). Soft computing based imputation and hybrid data and text mining: The case of predicting the severity of phishing alerts. Expert Systems with Applications, 39 (12), 10583–10589. https://doi.org/10.1016/j.eswa.2012.02.138

Rahman, O., Fung, B. C., & Liu, W. S. (2014). Using data mining to analyse fashion consumers’ preferences from a cross-national perspective. International Journal of Fashion Design, Technology and Education, 7 (1), 42–49. https://doi.org/10.1080/17543266.2013.864340

Reis, B., Miguel, R., Pereira, M., Azevedo, S., & Jerónimo, N. A. (2018, October). Genderless clothing issues in fashion. In  Textiles, Identity and Innovation: Design the Future: Proceedings of the 1st International Textile Design Conference (D_TEX 2017), November 2–4, 2017, Lisbon, Portugal  (p. 255). CRC Press. https://doi.org/10.1201/9781315100210-47 .

Reports and Data. (2020). Text Mining market forecasts to 2027 . Retrieved January 8, 2021, from https://www.reportsanddata.com/report-detail/text-mining-market

Rickman, T. A., & Cosenza, R. M. (2007). The changing digital dynamics of multichannel marketing. Journal of Fashion Marketing and Management: An International Journal., 11 (4), 604–621. https://doi.org/10.1108/13612020710824634

Risman, B. J., & Davis, G. (2013). From sex roles to gender structure. Current Sociology, 61 (5–6), 733–755. https://doi.org/10.1177/0011392113479315

Robinson, M. (2019). Two-Spirit identity in a time of gender fluidity. Journal of Homosexuality, 67 (12), 1675–1690. https://doi.org/10.1080/00918369.2019.1613853

Article   PubMed   Google Scholar  

Rocha, M. A. V., Hammond, L., & Hawkins, D. (2005). Age, gender and national factors in fashion consumption. Journal of Fashion Marketing and Management: An International Journal, 9 (4), 380–390. https://doi.org/10.1108/13612020510620768

Segalov, M. (2020, October 10). Hussein Chalayan: ‘Gender-free clothing is the future’ . The Guardian. Retrieved January 8, 2021, from https://www.theguardian.com/lifeandstyle/2020/oct/10/hussein-chalayan-gender-free-clothing-is-the-future

Shen, C. W., Chen, M., & Wang, C. C. (2019). Analyzing the trend of O2O commerce by bilingual text mining on social media. Computers in Human Behavior, 101 , 474–483. https://doi.org/10.1016/j.chb.2018.09.031

Shim, W. H. (2020, September 7). Korea’s social media penetration rate ranks third in world . The Korea Herald. Retrieved September 27, 2021, from http://www.koreaherald.com/view.php?ud=20200907000815

Shin, E. J., & Koh, A. R. (2020). Korean Genderless Fashion Consumers’ Self-image and Identification. Journal of the Korean Society of Clothing and Textiles, 44 (3), 400–412. https://doi.org/10.5850/JKSCT.2020.44.3.400

Sievert, C., & Shirley, K. (2014, June). LDAvis: A method for visualizing and interpreting topics. In  Proceedings of the workshop on interactive language learning, visualization, and interfaces  (pp. 63–70). https://doi.org/10.3115/v1/W14-3110 .

Szmydke, P. (2015, December 18). Top stories of the year: Going gender-neutral gender-neutral . WWD. Retrieved January 5, 2021, from https://wwd.com/fashion-news/designer-luxury/top-stories-of-the-year-going-gender-neutral-10299373/

Trice, A. (2015, November 23). The Future of cognitive computing . IBM. Retrieved February 8, 2021, from https://www.ibm.com/blogs/cloud-archive/2015/11/future-of-cognitive-computing/

Wallach, H. M. (2006, June). Topic modeling: beyond bag-of-words. In  Proceedings of the 23rd international conference on Machine learning  (pp. 977–984). https://doi.org/10.1145/1143844.1143967 .

Wertz, J. (2018, September 30). Online marketing as we know it is changing with the rise of generation Z . Forbes. Retrieved March 2, 2021, from https://www.forbes.com/sites/jiawertz/2018/09/30/online-marketing-as-we-know-it-is-changing-with-the-rise-of-generation-z/#5b5790df65ab

Xu, L., & Li, Y. (2012). Analysis of Social Change of Neutral Fashion. In  Soft Computing in Information Communication Technology  (pp. 449–453). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29148-7_63 .

Yang, S. M. (2020). A study of aesthetic characteristic by make-up image in the genderless cosmetics. Journal of the Korean Society of Cosmetology, 26 (6), 1344–1356. https://www.e-jkc.org/

Google Scholar  

Yoo, J. Y. (2020, February 26). “It’ not my girlfriend’s bag, it’s my handbag…Men need handbags, too . JoongAng Ilbo. Retrieved April 10, 2021, from https://news.joins.com/article/23715798

Yotka, S. (2020, December 16). Fashion shows are dead, long live fashion shows! Vogue. Retrieved January 5, 2021, from https://www.vogue.com/article/future-of-fashion-shows-2020-2021

Zhang, Y., Jin, R., & Zhou, Z. H. (2010). Understanding bag-of-words model: A statistical framework. International Journal of Machine Learning and Cybernetics, 1 (1–4), 43–52. https://doi.org/10.1007/s13042-010-0001-0

Zhang, C., Zeng, D., Li, J., Wang, F. Y., & Zuo, W. (2009). Sentiment analysis of Chinese documents: From sentence to document level. Journal of the American Society for Information Science and Technology, 60 (12), 2474–2487. https://doi.org/10.1002/asi.21206

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Acknowledgements

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This work was supported by the Ewha Womans University Research Grant of 2020.

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Hyojung Kim, Inho Cho & Minjung Park

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HK originated the research idea and carried out the research. IC discussed the results with HK and MP helped with the improvement of the manuscript. All authors read and approved the final manuscript.

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Hyojung Kim: Ph.D. candidate, Department of Fashion Industry, Ewha Womans University, 52, Ewhayeodae‑gil, Seodaemun‑gu, Seoul 03760, South Korea.

Incho Cho: Visiting Professor, Department of Fashion Industry, Ewha Womans University, 52, Ewhayeodae‑gil, Seodaemun‑gu, Seoul 03760, South Korea.

Minjung Park: Professor, Department of Fashion Industry, Ewha Womans University, 52, Ewhayeodae‑gil, Seodaemun‑gu, Seoul 03760, South Korea.

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Kim, H., Cho, I. & Park, M. Analyzing genderless fashion trends of consumers’ perceptions on social media: using unstructured big data analysis through Latent Dirichlet Allocation-based topic modeling. Fash Text 9 , 6 (2022). https://doi.org/10.1186/s40691-021-00281-6

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Trends in the Fashion Sector: An Analysis of Their Use and Paths for the Researcher Profession

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• Layla de Brito Mendes   ORCID: orcid.org/0000-0003-4982-9728 12 , 13 ,
• Ana Cristina Broega   ORCID: orcid.org/0000-0001-8400-8402 12 &
• Nelson Pinheiro Gomes   ORCID: orcid.org/0000-0003-3724-4044 14  

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Trend Studies are still going through their consolidation process as an academic approach. In contrast, trend research as a professional area is more consolidated and represents an essential link in the fashion chain, as they are helpful for the strategic planning of companies in the sector. The researcher's professional practice is constantly changing, as is the phenomenon of fashion, and follows changes in the technological, social, artistic, and cultural spheres that impact societies. Therefore, this paper is part of an exploratory research, with a qualitative approach, developed during an ongoing doctorate in fashion design. Through the methodologies of literature review and in-depth interviews, we present an overview of themes/subjects that have contributed to the redirection of the practices in trend analysis (especially those directed to the fashion sector) developed by the trend researcher as a professional.

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Online Trend Observation: Analysis of Free Content Available by Fashion-Directed Agencies

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Practices such as advanced design, design fiction, speculative design, e design activism are cited [3, p.2].

The authors adopt the designation given by Gomes et al. [ 30 ], where Trends Studies correspond to the academic approach of the field and trends research corresponds to the professional or commercial approach.

Gomes et al. [ 30 ] state that the definition of cool is complex, but it must be outlined within the research context to be developed.

The author understands that theoretical and critical discussions about the relationship between art and fashion are relevant from the perspective of the trend researcher's work, and thus presents four theses: the first, where fashion and art are irreconcilable and separate; the second, which considers fashion as constituted by artistic and functional elements; the third, to assume that fashion has a high cultural and symbolic value, similar to art; and the fourth, where fashion is seen as an art subject to restrictions, repeatable and ambivalent [13, p.422].

It is important to emphasize that accessing trends through trend books or trend reports developed by agencies corresponds to a financial investment to companies [24; 7], which is not always possible.

http://miro.com/

http://trello.com/

Morace, F.: O que é o futuro. Estação das Letras e Cores, São Paulo (2013)

Google Scholar  

WFSF Homepage. https://wfsf.org/about-futures-studies/ , Accessed 14 Feb 2022

Celi, M., Colombi, C.: Trends as future prompts in the anticipatory design practice. Futures 121 , 1–9 (2020)

Article   Google Scholar  

Gomes, N.P., Cohen, S.A.M., Flores, A.M.M.: Estudos de Tendências: contributo para uma abordagem de análise e gestão da cultura. Modapalavra e-periódico 11 (22), 49–81 (2018)

Powers, D.: On Trend: The Business of Forecasting the Future. University of Illinois Press, Champaign (2019)

Book   Google Scholar  

Dragt, E.: How to Research Trends. Bispublisher, Amsterdam (2017)

Mendes, L.B.: Metodologias de Coolhunting Aplicadas à Criatividade em Moda: Estudo de Caso Empresa Tendere. Masters Thesis. Universidade do Minho, Guimarães (2014)

Erner, G.: Sociologia das Tendências. Editora Gustavo Gili, São Paulo (2015)

Blaszczyk, R.L., Wubs, B.: Fashion futures. In: Blaszczyk, R.L., Wubs, B. (eds.) The Fashion Forecasters: A Hidden Story of Color and Trend Prediction, pp. 253–263 (2018)

Vicent-Ricard, F.: As espirais da moda, 2nd edn. Editora Paz e Terra, Rio de Janeiro (1989)

Campos, A.Q.: Expertise e legitimidade: os bureaux de style e a moda contemporânea. Revista Dobras 12 (27), 193–205 (2019)

Sant’Anna, P., Barros, A.R.: Pesquisa de tendências para moda. In: Anais do VII Colóquio de Moda. Abepem, Maringá (2011)

Pedroni, M.: Industrialized inspiration: reassessing the osmosis between fashion and art through the work of trend forecasters. In: Paulicelli, E., Manlow, V., Wissinger, E. (eds.) The Routledge Companion to Fashion Studies, pp. 421–427 (2021)

Monçores, A., Coutinho, T.: As profissões da pesquisa de tendências. In: Proceedings CIMODE 2016: 3º Congresso Internacional de Moda e Design, pp. 794–801. Universidade do Minho, Guimarães (2016)

Raymond, M.: The Trend Forecaster’s Handbook. Laurence King, London (2010)

Vejlgaard, H.: Anatomy of a Trend. Confetti Publishing, Schopfheim (2008)

Gladwell, M.: The coolhunt. The New Yorker (1997). https://www.newyorker.com/magazine/1997/03/17/the-coolhunt , Accessed 05 Oct 2021

Gloor, P.A., Cooper, S.M.: Coolhunting: Chasing Down the Next big Thing. Amacom, New York (2007)

Higham, W.: The Next Big Thing: Spotting and Forecasting Consumer Trends for Profit. Kogan Page, London (2009)

Baudrillard, J.: A troca simbólica e a morte. Edições Loyola, São Paulo (1996)

Calanca, D.: História social da moda. Editora Senac São Paulo, São Paulo (2008)

Kawamura, Y.: Fashionology: An Introduction to Fashion Studies. Berg Publishers, Oxford (2005)

Lipovetsky, G.: O império do efêmero: a moda e seu destino nas sociedades modernas. Companhia das Letras, São Paulo (2009)

Campos, A.Q.: Innovation, conformity and other ambivalences in fashion design. Modapalavra e-periódico 14 (33), 36–57 (2021)

Treptow, D.: Inventando moda: planejamento de coleções. Edição da Autora, São Paulo (2013)

Seivewright, S.: Pesquisa e design. Bookman, Porto Alegre (2009)

DeLong, M.: Critical thinking in design. In: Education and New Developments 2019 Proceedings, pp. 110–114. World Institute for Advanced Research and Science (WIARS), Porto (2019)

Lynch, A., Strauss, M.D.: Changing Fashion: A Critical Introduction to Trend Analysis and Meaning. Berg, Oxford (2007)

McCracken, G.: Culture and Consumption. Indiana University Press, Indiana (1988)

Gomes, N.P., Cohen, S.A.M., Cantú, W.A., Lopes, C.M.A.: Roteiros e modelos para a identificação de tendências socioculturais e a sua aplicação estratégica em produtos e serviços. Modapalavra e-periódico 14 (32), 229–272 (2021)

Kawamura, Y.: Doing Research in Fashion and Dress. Berg Publishers, Oxford (2011)

Souza, G.M.: O espírito das roupas: a moda no século dezenove. Companhia das Letras, São Paulo (1987)

Filho, J.G.: Gestalt do objeto: sistema de leitura visual da forma. Escrituras, São Paulo (2009)

Löbach, B.: Design Industrial: Bases Para a Configuração dos Produtos Industriais. Blucher, São Paulo (2001)

Holland, G., Jones, R.: Fashion Trend Forecasting. Laurence King, London (2017)

Frayling, C.: Research in art and design. In: Royal College of Art Research Papers, vol. 1, no. 1, pp. 1–5 (1993/4)

Gil, A.C.: Como elaborar projetos de pesquisa, 4 edn. Atlas, São Paulo (2002)

Gil, A.C.: Métodos e Técnicas de Pesquisa Social, 6 edn. Atlas, São Paulo (2008)

Taleb, N.N.: A lógica do cisne negro: o impacto do altamente improvável. Editora Best Seller, Rio de Janeiro (2008)

Schrobsdorff, S.: Who bought all these ridiculous clothes and why are they in my closet? Time (2021). https://time.com/6076269/who-bought-all-these-ridiculous-clothes-and-why-are-they-in-my-closet/ , Accessed 05 Oct 2021

Fisher, R.: Time capsule 2020: the 37 objects that defined the year. BBC (2020). https://www.bbc.com/future/article/20201217-time-capsule-2020-the-37-objects-that-defined-the-year , Accessed 05 Oct 2021

Farah, S.H.: What will be cool this summer? the cut (2021). https://www.thecut.com/2021/04/covid-pandemic-future-of-main-characters.html , Accessed 05 Oct 2021

Hyland, V.: Do we really want fashion to ‘go back to normal?’ ELLE (2021). https://www.elle.com/fashion/a36477027/post-pandemic-fashion/ , Accessed 05 Oct 2021

Morgan, B.: In a post-covid world customers will be revenge shopping. Forbes (2021). https://www.forbes.com/sites/blakemorgan/2021/03/22/customers-are-ready-to-back-with-covid-revenge-shopping/?sh=13b3dd8030ab , Accessed 05 Oct 2021

Saunter, L.: Post-covid-19 retail: revenge buying. WGSN (2020). https://www.wgsn.com/blogs/post-covid-19-retail-revenge-buying/ , Accessed 05 Oct 2021

Sedoikina, E.: How will trend forecasting look post-covid? Birmingham city institute of jewellery, fashion and textiles. https://www.bcu.ac.uk/fashion-and-textiles/news-and-events/blog/how-will-trend-forecasting-look-post-covid , Accessed 05 Oct 2021

Arnett, G.: Trend forecasters predict a more trendless future. Vogue Business (2020). https://www.voguebusiness.com/fashion/trend-forecasters-predict-a-more-trendless-future-covid-19 , Accessed 05 Oct 2021

Wharry, G.: Why trends ARE your friend. Medium (2020). https://medium.com/fashion-act-now/why-trends-are-your-friend-781b718c2715 , Accessed 05 Oct 2021

Future Resources Homepage. https://www.lydiacaldana.com/futureresources , Accessed 05 Oct 2021

Bielskyte, M.: Protopia futures [framework]. Medium (2021). https://medium.com/protopia-futures/protopia-futures-framework-f3c2a5d09a1e , Accessed 05 Oct 2021

UNESCO: what is futures literacy (FL)? https://en.unesco.org/futuresliteracy , Accessed 05 Oct 2021

Preira, J.: Gli studi di Futuri spiegati a mia figlia. TEDxBarletta, set (2020). https://www.ted.com/talks/joice_preira_gli_studi_di_futuri_spiegati_a_mia_figlia , Accessed 05 Oct 2021

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de Brito Mendes, L., Broega, A.C., Gomes, N.P. (2023). Trends in the Fashion Sector: An Analysis of Their Use and Paths for the Researcher Profession. In: Raposo, D., Neves, J., Silva, R., Correia Castilho, L., Dias, R. (eds) Advances in Design, Music and Arts II. EIMAD 2022. Springer Series in Design and Innovation , vol 25. Springer, Cham. https://doi.org/10.1007/978-3-031-09659-4_23

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Fashion trends and their impact on the society

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Nithyaprakash venkatasamy

Modapalavra

Amanda Queiroz Campos

With the intention of contributing to the theoretical deepening of trend studies, the present research aims to understand the historical origins and transformations of the term trend and, more specifically, the fashion trend. The methodological procedure involved a consistent bibliographical review. Firstly, we investigated the etymology of the term in Portuguese, English, and German. In addition, we combined different consolidated bibliographical references - such as Caldas, Lindkvist, and Vejlgaard - in order to historically understand the concept and its applicability. The general interpretation of the term tendency relates to a force that leads to a finite but uncertain future. Between 1946 and 1975, trends acquired a comprehension similar to their current meaning, emphasizing their economic relevance, which coincides not arbitrarily with the birth of mass consumption. The contemporary connotation associates trends to changes and transformations that involve different sociocultural and economic aspects. In turn, fashion trends are expressions of sociocultural tendencies in visual and tactile characteristics applied to fashion products.

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Fashion fills all cells of society only there, and where there is a possibility that some social groups or classes may imitate others by imparting separate cultural patterns. If we try on the basis of a past experience of comparing fashion as something substantive, we can say that in the twenty-first century it is striving for gradual institutionalization and this is joined by the field of sociology on one hand, and on the other of art objects.Fashion has the function of a social regulator, demonstrating social inequality but also smoothing the differences between social groups. It is not only a means of demonstrating social status, but also a means of impact. Based on the socio-psychological mechanisms of fashion influence, images and situations can be created that can generate certain ideas, some of which are in the field of fashion and the circle becomes a self-sustaining eternal reality, as the society to which they serve

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Fashion is ever changing and revitalized from the soul of the designers. Trend is a collection of style that are seen on a large number of people. This paper envisions the ideas and collections around the globe including the domestic market. Upcoming style quotient is made from understanding the current popular styles. The full sleeve kameez when reaching a saturation declines to welcome the sleeveless styles into the future market. This paper encompasses of all the trends in both domestic and international market with regard to costumes and accessories. Home science, fashion designing, clothing and textile students might benefit from gaining knowledge about the current trends to frame their wardrobe in current style and also understand the basics for their future fashion designing carrier.

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The behavior of following the most in vogue and admired styles of dressing has been there since the ancient times. With time, people have moved on with the rapid change in the surroundings. Both the genders acknowledge fashion. Fashion is an extension of regular clothing. This paper investigates the role of economical players which includes per capita income and inflations in shaping up the various trends/ fashion trends/ trends in clothing and their consumptions for Pakistan, India, United States and Australia. The findings of this paper confirms that the outline economical players do matter for various popular trends of Pakistan, India and United States, while the same economical players they don’t really matter for shaping up the fashion trends in Australia, this suggest that these nations have different socio economical conditions along with the various different modes of lifestyles which are mattered for various categories of reasons.

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