The Role Of Big Data And Fashion Sustainability Transparency On Gen Y And Gen Z’s Consumer Consumption Behavior

The Use of Social Networks and Advertising on Consumer Behaviour and Customer Loyalty

Research Topic: The role of big data (social media) and fashion sustainability transparency in gen Z and gen Y’s consumer consumption behaviour.

In the modern world, social media is a web-based application which is playing an important role in human lives. The use of social networks is a much more quick and convenient approach to access and share information. With the use of social networks, it is also possible to communicate with any of the individuals of any location on a virtual level. Advertising is an effective approach to reach out to clients and prospects. It has an effect on consumer behaviour and customer loyalty. Interaction between firms and consumers via social networking sites has demonstrated to produce significant effects (Enginkaya and Y?lmaz, 2021).

From the viewpoint of customers, cloud technology and openness in respect to social media advertising are equally crucial (Venkatesh et al. 2020). Social media advertising is an online marketing technique that entails developing and sharing content on social media websites. Transparency, on the other hand, can be a huge issue that affects consumer trust. Customer information should be created for trust and openness in social media advertising. According to Song and Lee (2016), technology advancements, such as big data, make it easier for businesses to collect large volumes of client data. However, it has been observed that they are secretive about the data they collect.

Consumers are also more concerned about the risk of their data being resold. Though it may be simpler for businesses to benefit in the immediate term, it will weaken customer loyalty and behaviour in the long run. As a result, it will impede long-term competition. It is critical for businesses to consider employee attitudes about integrity and confidentiality. According to Egels-Zandén and Hansson (2016), data privacy and security will decide how credible a firm is to customers. In order to develop trust, brands must be upfront about the information they collect.

It’s also critical to provide customers with adequate cash in exchange for their information. In the field of social media advertising of sustainable among high end fashion labels, cloud computing and openness may have an impact on Gen Y and Gen Z customers’ confidence and attitude. The focus of this research design will be on new products in the fashion industry marketplace. Several customers are also ready to pay more just for luxury goods if the company is determined to make a beneficial influence on the environment and society (Sorensen and Johnson Jorgensen, 2019). Customers should trust that an organization understands about the confidentiality and protection of their information if it has a clear data strategy. In this environment, it is impossible to deny that global luxury firms rely heavily on massive data to gather customer data for social media management purposes (Ladhari, Gonthier, and Lajante, 2019).

Fuchs (2018) expressed his opinions and research that social media marketing and big data are turn out to be pervasive keywords in current expectancy of life because big data help in approaching the potential customers in relevance with the particular brand or product. In addition, the use of big data constantly increasing the customer base for every organisation which is significantly support by the social media. The organisation uses social media marketing procedures to acquire data of their targeted population. Henceforth, the usage of social media marketing is the most frequent and collecting the data. It significantly helps in maximising the brand worth by increasing customer base.

Transparency and Customer Trust in Social Media Advertising

The role of social media is highly influencing the work life and social life of people. It also influencing the organisations to adopt the use of social media in order to increase the customer reach. In addition, the use of big data in the social media marketing is again an advantageous practice for increasing the customer base for fashion industry (Huber et al., 2019). Big data is a data which consists better variability of information, arriving in maximising volumes along with added velocity. It effectively helps in supporting the business for targeting their potential customers and influence them through the help of social media marketing (Ghani et al., 2019).

In accordance with this study, the researcher is aiming to identify the use of big data in the social media marketing by fashion industry and its influence on Gen Y and Z as they are the foremost targeted population for fashion organisations. This concern has to be resolved in this study, for which the study planned to conduct a survey and collect information from the customers those are Gen Y and Z.

Aim Of The Study

The aim of this study is to examine the role of social media and fashion sustainability transparency. The study is focusing on exploring the big data in social media particularly. In addition, the study is examining the role of big data in social media and fashion sustainability transparency in gen Z and gen Y’s consumer buying behaviour. It also aimed to observe the big data on trust and behaviour of Gen Y and Gen Z consumers in the context of social media marketing of sustainability in the luxury fashion brand sector.

Objective Of The Study

• To observe the influence of big data in social media on the fashion industry
• To examine the role of big data in social media on Gen Z and Y’s consumer buying behaviour
• To evaluate the impact of fashion sustainability transparency on the Gen Z and Y’s consumer buying behaviour

The research question of the study is as follows:

Question. What is the role of big data in social media marketing and fashion sustainability transparency in gen Z and gen Y’s consumer consumption behaviour?

Social media is considered as the tool to maximise the communication within people. They can communicate with everyone and anywhere as social media consists of a wide range based on the internet technology. This practice facilitates organisations to share information, content, influence customers, and increase communication with the medium of virtual network (Qin, 2020). Additionally, the use of big data is again playing a significant role in social media marketing when it comes to increase the reach of customers. The use of big data consists better variability of information, arriving in maximising volumes along with added velocity (Ghani et al., 2019).

Therefore, the rational of the study presents that it is necessary for this study to build an understanding about the social media marketing of sustainability (Zhao and Copeland, 2019). In addition, it also the researcher will examine the role of social media and fashion sustainability transparency. The study is also focusing on exploring the big data in social media particularly. In addition, the study is examining the role of big data in social media and fashion sustainability transparency in gen Z and gen Y’s consumer buying behaviour. It also aimed to observe the big data on trust and behaviour of Gen Y and Gen Z consumers in the context of social media marketing of sustainability in the luxury fashion brand sector.

Consumer Concern Over Data Resale and Long-Term Competition

Social media: It is an internet-based application which plays a vital part in the lives of people in current scenario. The usage of social media is way easier as well as convenient source for accessing and providing information. It also helps in communicating with anyone at any place on virtual level with the use of social media (Duggan et al., 2015).

Consumer buying behaviour: It refers to the actions taken by consumers on the basis of the product or service before buying it. The procedure of consumer buying behaviour might involve engaging the posts on social media (Pappas, 2016).

Gen Z: This is the generation who was born between 1997 and 2012 (Sladek and Grabinger, 2014).

Gen Y: This is the generation who was born between 1981 and 1996 (Black, 2010).

Big Data: It is a data which consists better variability of information, arriving in maximising volumes along with added velocity (George, Haas, and Pentland, 2014).

The overall paper of this study is encompassing five chapters in total. In this, the first chapter is introductory part which consists of essential information related to the topic. In addition, it involves the purpose of conducting this study along with significance of researching this particular subject. 2^nd chapter is literature review that is incorporating several related literatures those are helping the research matter to understand its actual concept. For instance, related to big data usage in social media, its impact on fashion industry, and many more. The third chapter is research methodology which is allowing researcher to choose appropriate methods and techniques to collect and analyse a reliable and valid data for obtaining significant results. Then the 4^th chapter is the data analysis and presentation of findings. This chapter will present the survey responses through descriptive statistical method and also test the hypothesis through regression, etc. At last, the 5^th chapter involves the summary of the entire paper along with the recommendations for future study.

The initial section of this study is covered by the literature review that is incorporating several related literatures those are helping the research matter to understand its actual concept. It is facilitating researcher to involve several adequate literatures those are relevant with the topic and underlying research problem. These academic literatures will also contribute in addressing the aims and purpose of this study. This section effectively examined the influence of big data in social media on the fashion industry with the help of relevant literatures. It also examined the role of big data in social media on Gen Z and Y’s consumer buying behaviour. In addition, it similarly evaluated the impact of transparency in sustainable fashion on the Gen Z and Y’s consumer buying behaviour. At the same time, secondary sources have been used in this study such as journals, online sources, news, articles, books, and more. This literature review is formed with the purpose of supporting the further responses of participants as an evidence.

Figure 1: Conceptual Framework

(Source: By researcher)

The above conceptual framework is designed with the purpose of provide an understanding about how big data plays an effective role in increasing the customer base and this practice is done with the help of social media marketing. The use of social media marketing is the foremost major for gathering a big data for fashion industry and use it for targeting the potential customers. In addition, this conceptual framework is being used in the literature review for adding relevant literatures those are adequate for examining the use of social media marketing in big data and its impact on the Gen Y and Z in respect with fashion sustainability as well.

The importance of Data Privacy and Security for Customer Trust

Li, Larimo, and Leonidou (2021) examined from their study that social media marketing strategies helps in achieving competitive advantages and increases the organisational performance. In this, the researchers used survey as well as interview technique to acquire information from the managers of social media marketing of different organisations. The results of this study highlighted that the use of social media marketing techniques is highly effective for increasing the performance of the organisation. It effectively helps in reaching to the targeted customers which adequately increases the competitive advantage and support organisation in future sustainability.

In addition, Chatterjee and Kar (2020) stated that social media is a web-based application which is playing an important role in human lives, in the current world. The use of social networks is a much more quick and convenient approach to access and share information. With the use of social networks, it is also possible to communicate with any of the individuals of any location on a virtual level. Advertising is an effective approach to reach out to clients and prospects. It has an effect on consumer behaviour and customer loyalty. Interaction between firms and consumers via social networking sites has demonstrated to produce significant effects.

Other than this, the study of Jacobson, Gruzd, and Hernández-García (2020) is based on the social media marketing in order to observe the customer perception towards it. In this, 751 sample size has been used those were the consumers who shared their viewpoint with the help of online survey. The findings of the study show that the perceived risks of customers and the advantages of using social media have a significant relationship. This study also examines that the use of social media marketing strategies enhances the way of communication which helps customers to reach to the customer services of any particular organisation through online medium and resolve their concern easily. It increases the loyalty of customers towards the product or brand which is a competitive advantage.

The study of Sivarajah et al. (2020), the use of technology is enhancing and the usage of social media advertising pushes businesses to collect and send precise data about their customers in attempting to manipulate their purchasing habits. Some companies are transparent about their data gathering tactics, whereas others continue to take their customers in the obscurity. This study also highlighted that technology advancements, such as big data, make it easier for businesses to collect large volumes of client data. However, it has been observed that they are secretive about the data they collect. Other than this, consumers are also more concerned about the risk of their data being resold. Though it may be simpler for businesses to benefit in the immediate term, it will weaken customer loyalty and behaviour in the long run. As a result, it will impede long-term competition. It is critical for businesses to consider employee attitudes about integrity and confidentiality (Jain, Gyanchandani, and Khare, 2019).

Other than this, the study of Fuchs (2018) expressed his opinions in his book that social media marketing and big data are considered as the universal keywords in present lifespan because big data help in approaching the potential customers in relevance with the particular brand or product. Additionally, the use of big data constantly increasing the customer base for every organisation which is significantly support by the social media. The organisation uses social media marketing procedures to acquire data of their targeted population. Hereafter, the usage of social media marketing is the most frequent and collecting the data. It significantly helps in maximising the brand worth by increasing customer base.

The Influence of Social Media and Big Data on Business and Customer Base

Other than this, from the perception of customers, the use of cloud technology and openness in respect to social media advertising are equally crucial. Social media advertising is an online marketing technique that entails developing and sharing content on social media websites. Transparency, on the other hand, can be a huge issue that affects consumer trust. Customer information should be created for trust and openness in social media advertising (Liu, Shin, and Burns, 2021).

Consumers’ sensitive data is also collected by some brands for future usage. In addition, the study of Forbath and Schoop (2018) added that the information can be collected and sent via usage of connectivity of things and artificial intelligence. As a result, it gives community and administration innovative improvements. Companies that realize how much information is worth to customers may provide comparable benefits in return. Transparency in the exchange will become increasingly vital in establishing trust (Harvard Business Review, 2018).

In this section, the study is focusing on Generation Z and Generation Y. In this, Gen Z is the generation who was born between 1997 and 2012 whereas the generation Y are those who was born between 1981 and 1996 (Sladek and Grabinger, 2014). However, the consumer buying behaviour is refers to the actions taken by consumers on the basis of the product or service before buying it. The procedure of consumer buying behaviour might involve engaging the posts on social media (Pappas, 2016). Nevertheless, this section is only focusing on the consumers those are belongs to Gen Z and Gen Y, in respect with the problem of this research paper. At the same time, this section is examining the influence of social media on Gen Z and Gen Y. Similarly, it is also observing the role of big data on both the generations – that how it has impact on consumers’ behaviour while using social media.

Social Media Marketing On Gen Z And Gen Y

The research done by Djafarova and Bowes (2021) examined that the tools of Instagram marketing are found as most effective for influencing the buying behaviour of Gen Z in the fashion industry. Generation Z customers are those who are born in 1997 and 2012 and their ages are between 10 and 25. These customers are the targeted customers for fashion organisations because they highly active on Instagram in current scenario and follow influencers for adhering new trend. It helps in promoting the products of fashion organisation such as cloths, accessories, and more. This helps in increasing the customer reach and increasing the demand of that particular brand or product. At last, the study revealed that the use of Instagram marketing is effectively found as beneficial for influencing the buying behaviour of Gen Z in the fashion industry.

According to Lee (2017), customers must be aware of who controls their data. It also described that transparency is crucial. Many businesses, however, are wary of information privacy legislation, fearing that they may damage their business strategies. Customers provide their approval for luxury fashion firms to collect their data. Understanding the Gen Y and Gen Z consumption patterns will also be important for new brands entering the fashion industry. In recent times, two significant components, social networks and sustainable development, have dominated the business sector. It has aided in the improvement of consumer engagement and has altered how businesses view success.

Research Design and Objectives

Big Data (Social Media) On Gen Z And Gen Y

As per the study of Du et al. (2018), the data privacy and security will decide how credible a firm is to customers. In order to develop trust, brands must be upfront about the information they collect. Other than this, Muhammad, Dey, and Weerakkody (2018) highlighted that big data make it easier for businesses to gather large volumes of data of potential clients. However, it has been observed that they are secretive about the data they collect. In simple words, the data collected from the social media tools such as Instagram, Facebook, YouTube, etc. has to be secured and keep it private. Furthermore, the study reveals big data contributes in examining the potential customers for the organisation in relevance with the particular product. In respect with the study of Ladhari, Gonthier, and Lajante (2019); Dabija, Bejan, and Dinu (2019), it has been observed that Gen Z and Y are the most potential customers for fashion products. With the use of social media tools, it is easy to identify the targeted customers.

In addition, the study of Esteban-Santos et al. (2018) is conducted on fashion bloggers, those are influencers and help fashion organisations to influence the customers towards their products. This study examines that how fashion influencers have impact on the buying behaviour of Millennials. Additionally, this study used quantitative method for collecting survey responses and use IBM SPSS Statistics for identifying the relationship between fashion bloggers and consumer buying behaviour of Millennials. The overall study reveals that fashion bloggers has a significant and positive relationship with the consumer buying behaviour of Millennials. In addition, it concludes that the fashion organisations effectively use the help of fashion bloggers or influencers in order to promote their brand and products on social media. This results in increasing the reach of customers and help organisation to increase their performance as well. This practice is profitable for the organisation and similarly maximises the competitive advantages.

Gazzola et al. (2020) had conducted a study on trends in the fashion industry. There major focus was on the perception of sustainability as well as on the generation. However, this study also observes that it is also critical to provide customers with adequate cash in exchange for their information. In the field of social media advertising of sustainable among high end fashion labels, cloud computing and openness may have an impact on Gen Y and Gen Z customers’ confidence and attitude. The focus of this research design will be on new products in the fashion industry marketplace. Several customers are also ready to pay more just for luxury goods if the company is determined to make a beneficial influence on the environment and society. Customers should trust that an organization understands about the confidentiality and protection of their information if it has a clear data strategy. In this environment, it is impossible to deny that global luxury firms rely heavily on massive data to gather customer data for social media management purposes. Other than this, the results of the study shows that Gen Z and Y are potential customers for fashion industry as they prefer trending collection which helps in increasing the organisational sustainability as well.

Sun and Wang (2019) highlighted that sustainable social media advertising will be critical in supporting consumers in purchasing items from firms that adopt corporate sustainability methods. In this regard, it’s also worth noting that social media advertising for sustainable will enable consumers in the fashion industry to move to new luxury companies. According to Chu, Chen, and Gan (2020), this will not only improve the brand’s position in the market, but also enhance customer faith in the company. Consumers trust brands that use sustainable business methods. They believe the company’s business procedures are open and straightforward. As an outcome, the customer attitude toward such a company improves for the better. As a result, using social networks to reach out to customers on a personalized and engaged level is an important social media advertising technique.

This study effectively incorporated the understanding of the influence of big data in social media on the fashion industry and examined the role of big data in social media on Gen Z and Y’s consumer buying behaviour. It also evaluated the impact of fashion sustainability transparency on the Gen Z and Y’s consumer buying behaviour. In this, the researcher effectively found some gap in this chapter of literature review that there is lack of studies involved on the particular topic of the role of big data (social media) and fashion sustainability transparency in gen Z and gen Y’s consumer consumption behaviour. Therefore, it is necessary to provide a deep understanding and knowledge about this particular topic. Therefore, the researcher adequately conducted this study and focusing on identifying the relationship between all the variables and analyse it with the use of regression.

The methods and design of research has been charted in this section and it permits researcher to pick effective and proper methods and techniques to collect and analyse a reliable and valid data for obtaining significant results (Pandey and Pandey, 2021). These methods and techniques have been selected with the aim of gaining an insight about the subject of big data impact on the fashion industry and its targeted population, that is Gen Z and Y. In addition, how the usage of big data helps social media to influence the consumer buying behaviour towards the services and product of fashion industry.

In regards with the research concern, the researcher aimed to use paradigm research philosophy for obtaining accurate and adequate data related to the topic and its concern. In addition, the use of positivism research philosophy helps in identifying the causes and effects involved in the research matter and examine the relationship between the variables (Ryan, 2019). However, the use of interpretivism and realism philosophies are also effective at their place, but they are not adequate for this study because interpretivism philosophy is highly dependent and based on the views, principle, and interest of the researcher as the researcher plays a precise role to observe the social world. On the other hand, the use of positivism research philosophy is highly focused on acquiring factual knowledge about the research matter while using scientific methods and procedure (Žukauskas, Vveinhardt, and Andriukaitien?, 2018).

In addition, this study is aiming to examine the role of the big data and consumer buying behaviour in the context of social media marketing. The use of this philosophy and its unique features are effective in contributing to achieve the desired objective. Moreover, there are lack of studies available where the study is focusing on the big data in social media and fashion sustainability in influencing the consumer buying behaviour, especially of Gen Y and Gen Z. For this, positivism research philosophy is adequate for identifying the relationship between the variables (Ryan, 2019).

For bringing a desired outcome for the research, the technique and structure of the study is examined by the research design. There are three different types of research designs such as descriptive, exploratory and explanatory. In this, descriptive research design is adequate for the study where the study simply has to answer when, what, where, and how types of questions. It adheres simple systematic phenomenon of meeting the aims and objectives of the study (Taguchi, 2018). Besides, the use of exploratory design is adequate with secondary data collection method where the researcher has to collect existing and prior studies. In addition, the use of explanatory research design is adequate for researching and conducting such study which is not before done effectively (Ragab and Arisha, 2018).

However, this paper is focusing on the concept of social media and role of big data in it which is common research matter, but the focus of entire study is on the role of big data (social media) and fashion sustainability transparency in gen Z and gen Y’s consumer consumption behaviour – as there is lack of studies involved on the particular topic. Hence, the use of explanatory research design is significant for this study to identify the impact of big data in social media marketing on the transparency of consumers buying behaviour in the fashion industry (Prasad et al., 2017).

Considering these methods, the use of hypothesis is involved in this study. Therefore, deductive research approach has been used in this study for developing a hypothesis in this study instead of using inductive approach because inductive method is effective with the study where there is no involvement of hypothesis testing (Armat et al., 2018). Hence, deductive research approach has been chosen for the analysis of the role of big data in the social media and its impact on the behaviour of the Gen Y and Gen Z consumers in the context of fashion industry.

H1_a: A big data in social media marketing influence consumer buying behaviour of Gen Z and Y in fashion industry 

H1_b: A big data in social media marketing does not influence consumer buying behaviour of Gen Z and Y in fashion industry

H2: Big data in social media marketing has a significant impact on the fashion sustainability

H3_a: A fashion sustainability influence consumer buying behaviour of Gen Z and Y in fashion industry 

H3_b: A fashion sustainability does not influence consumer buying behaviour of Gen Z and Y in fashion industry

The study has chosen 150 as a sample size for targeting the managers, administrators, and employees those are working in the fashion industry and uses big data practice for social media marketing. At the same time, considering the research study, the researcher has chosen random sampling technique for targeting the potential population in this study. The participants are those who are working in fashion industry and increases customer base of their particular organisation through big data. In regards with this, using probability sampling method for targeting random participants is adequate choice. 150 is a minimum sample size for this study (Berndt, 2020). Moreover, the researcher has to target the participants with the practice of social media tools, emails, etc. The relatives, friends, and family of the researcher also contributed their efforts in this study to help researcher in finding out potential candidates for the study.

In accordance with the explanatory design and positivism research philosophy, the study is using quantitative method for collecting numerical information through survey technique. Additionally, the use of quantitative method is beneficial for this study to acquire adequate data for analysis in order to identify the relationship between the variables (Goertzen, 2017). Other than this, there are two types of data collection procedure. First is secondary data collection type. In this, the study has to focus on the existing and prior studies. Here, second data collection method is primary in which the researcher has to focus on collecting fresh and adequate information through reliable source such as survey, interview, etc. which helps in increasing the validity of the information (Wilson and Fox, 2013).

According to this research paper, the study is focusing on collecting primary data for gathering information from participants on the use of big data in social media marketing in order to influence the Gen Y and Gen Z consumers towards the fashion brand. The study is using survey technique for collecting numerical data for the analysis purpose. The researcher will collect information while forming questionnaire and spread it to the participants through Google Forms.

The study is using numerical data for identifying the relationship between the variables and test the hypothesis. According to Manetti and Bellucci (2016), the researcher is using descriptive statistical analysis method first for presenting the responses in tables and charts. Moreover, it also helps in interpretating the numerical and statistical data in detail method. Other than this, the study has to test the hypothesis for which correlation analysis and regression analysis is adequate for identifying the relationship between the variables. For this analysis, the researcher is using IBM SPSS statistical tool for meeting the objectives and resolving the research concern.

This study is conducting a primary research for collecting numerical information from survey technique; hence, some limitations, constraints and restrictions are definitely involved in this study (Giampietri et al. 2018). The primary study took more time, efforts, and costs in comparison of secondary data collection. Additionally, the sample size for this study is limited as it is difficult to collect information from the entire population; therefore, the researcher involved a particular area for collecting responses.

Conclusion Of Methodology 

The above chapter concludes that the use of positivist research philosophy, deductive approach, quantitative method, explanatory research design, primary data collection method, and random sampling technique are adequate selections. Moreover, the above chapter summarises that descriptive statistical analysis, correlation, and regression analysis are effective for this study to analyse and test the data.

The analysis of the chapter is presented here where the tables are incorporating the numerical or quantitative data which has been gathered from survey through questionnaire. In addition, the presentation of results is statistically involved in this study while using charts and the study interpretated it descriptively. Simply, the chapter 4 is the data analysis and presentation of findings. This chapter also present the survey responses through descriptive statistical method and also test the hypothesis through regression, etc. The survey sample size is 150 where all the participants had contributed their efforts, perception, and experience in relevance with the research matter. The participants are those who are working in fashion industry and increases customer base of their particular organisation through big data. In this, descriptive statistical analysis, correlation, and regression analysis are used for testing hypothesis and resolving the research question.

Descriptive Statistical Analysis Of Survey Responses

This section is presenting survey responses in the form of tables and charts. Moreover, the section is also interpretating the statistical data in descriptive manner in order to give an understanding about the responses of participants.

Table 1: Gender

What is your gender?
Male	88
Female	62

Figure 2: Gender

In the survey, 58.7% of the males has been participated and contributed their efforts, perception, and experience in relevance with the research matter. Moreover, rest 41.3% of the participants are female in this study. Both male and female shared their views and opinions associated to the practice of big data in social media marketing and its influence on Gen Y and Z buying behaviour in the fashion industry.

Table 2: From how many years participants are working with fashion industry

From how many years you are working with fashion industry?
Less than 3 years	55
3 – 5 years	49
6 – 8 years	31
More than 8 years	15

Figure 3: From how many years participants are working with fashion industry

In this, the researcher asked about the number of years participants are working with the fashion industry. The participants responded that 36.7% of them were having less than 3 years of experience with the fashion industry and 32.7% of them were having experience between 3 and 5 years. Additionally, 20.7% of the participants are having 6 to 8 years of experience with the fashion industry; similarly, remaining 10% are of more than 8 years of experience. It shows that participants were having different level of experience in the fashion industry.

Table 3: Is participants’ targeted population being Gen Z and Gen Y majorly

Is your targeted population being Gen Z and Gen Y majorly?
Yes	83
No	42
Maybe	25

Figure 4: Is participants’ targeted population being Gen Z and Gen Y majorly

The above stated responses shows that 53.3% believes that their targeted population being Gen Z and Gen Y majorly. However, 16.7% participants are saying that somewhere these are the potential participants and rest are not agreeing with the statement. But as per the majority of the respondents, the Gen Y and Z are the potential targeted population for the fashion industry.

Table 4: Is big data make it easier for businesses to collect large volumes of client data

Do you think big data make it easier for businesses to collect large volumes of client data?
Yes	83
No	42
Maybe	25

Figure 5: Is big data make it easier for businesses to collect large volumes of client data

The above stated responses shows that 53.3% are agreeing with the statement that big data make it easier for businesses to collect large volumes of client data. Although, 28% are not agreeing with the statement and they chosen ‘no’ option but rest of the 16.7% had chosen ‘maybe’ option as they believe that ‘yes’ big data make it easier for businesses to collect large volumes of client data.

Table 5: Is the use of big data by fashion industry is increasing their customer base

Do you agree that the use of big data by fashion industry is increasing their customer base?
Strongly agree	48
Agree	46
Neutral	26
Disagree	16
Strongly disagree	14

Figure 6: Is the use of big data by fashion industry is increasing their customer base

The results of the responses shows that 32% are strongly agreeing and 30.7% are agreeing with the statement that the use of big data by fashion industry is increasing their customer base. Although, there are some participants those are not agreeing with the statement and they chose ‘strongly disagree or disagree’ option but rest of the 17.3% had chosen ‘neutral’ option as they believe that ‘yes’ the use of big data by fashion industry is increasing their customer base.

Table 6: Are participants using social media for targeting potential customers towards participants’ fashion products

Are you using social media for targeting potential customers towards your fashion products?
Yes	98
No	34
Sometimes	18

Figure 7: Are participants using social media for targeting potential customers towards participants’ fashion products

The outcome of the responses shows that 65.3% are agreeing with the statement that using social media for targeting potential customers towards your fashion products. Even though, there are 22.7% of the participants those are not agreeing with the statement and they chose ‘no’ option but rest of the 12% had chosen ‘neutral’ option as they believe that somewhere using social media for targeting potential customers towards your fashion products. In this, majority of the participants are showing positive response.

Table 7: Is big data in social media marketing influence consumer buying behaviour of Gen Z and Y in fashion industry

Do you agree that big data in social media marketing influence consumer buying behaviour of Gen Z and Y in fashion industry?
Strongly agree	58
Agree	51
Neutral	21
Disagree	14
Strongly disagree	6

Figure 8: Is big data in social media marketing influence consumer buying behaviour of Gen Z and Y in fashion industry

The results of the responses shows that 34% are strongly agreeing and 38.7% are agreeing with the statement that big data in social media marketing influence consumer buying behaviour of Gen Z and Y in fashion industry. Although, there are some participants those are not agreeing with the statement and they chose ‘strongly disagree or disagree’ option but rest of the 14% had chosen ‘neutral’ option as they believe that ‘yes’ big data in social media marketing influence consumer buying behaviour of Gen Z and Y in fashion industry.

Table 8: Is the use of big data has an impact on improving fashion sustainability

Do you agree that the use of big data has an impact on improving fashion sustainability?
Strongly agree	53
Agree	48
Neutral	31
Disagree	13
Strongly disagree	5

Figure 9: Is the use of big data has an impact on improving fashion sustainability

The results of the responses shows that 32% are strongly agreeing and 35.3% are agreeing with the statement that the use of big data has an impact on improving fashion sustainability. Even though, there are some participants those are not agreeing with the statement and they chose ‘strongly disagree or disagree’ option but rest of the 20.7% had chosen ‘neutral’ option as they believe that somewhere the use of big data has an impact on improving fashion sustainability.

Table 9: Is social media marketing helps in achieving competitive advantages and promotes sustainability in fashion brand sector

Do you agree that social media marketing helps in achieving competitive advantages and promotes sustainability in fashion brand sector?
Strongly agree	58
Agree	52
Neutral	22
Disagree	13
Strongly disagree	5

Figure 10: Is social media marketing helps in achieving competitive advantages and promotes sustainability in fashion brand sector

The results of the responses shows that 34.7% are strongly agreeing and 38.7% are agreeing with the statement that social media marketing helps in achieving competitive advantages and promotes sustainability in fashion brand sector. Even though, there are some participants those are not agreeing with the statement and they chose ‘strongly disagree or disagree’ option but rest of the 14.7% had chosen ‘neutral’ option as they believe that somewhere social media marketing helps in achieving competitive advantages and promotes sustainability in fashion brand sector. In this, majority of the participants are showing positive response.

Table 10: Is the sustainable fashion transparency influence consumer buying behaviour of Gen Z and Y

Do you agree that the sustainable fashion transparency influence consumer buying behaviour of Gen Z and Y?
Strongly agree	54
Agree	45
Neutral	30
Disagree	15
Strongly disagree	6

Figure 11: Is the sustainable fashion transparency influence consumer buying behaviour of Gen Z and Y

The results of the responses shows that 36% are strongly agreeing and 30% are agreeing with the statement that the sustainable fashion transparency influence consumer buying behaviour of Gen Z and Y. Even though, there are some participants those are not agreeing with the statement and they chose ‘strongly disagree or disagree’ option but rest of the 20% had chosen ‘neutral’ option as they believe that somewhere the sustainable fashion transparency influence consumer buying behaviour of Gen Z and Y. In this, majority of the participants are showing positive response.

Correlation Analysis 

Table 11: Correlation analysis

Correlations
	Gender	From numbers of years participants working with fashion industry	Is participant’s target Gen Z and Gen Y majorly	Big data make easier to collect large volume of data	Use of big data by fashion industry increasing customer base	Participants using social media for targeting customers towards their product	Big data in social media marketing influence Gen Z and Y behaviour	Use of big data has impact on improving fashion sustainability	Social media marketing helps achieving competitive advantage and promotes sustainability	Sustainable fashion influence buying behaviour of Gen Z and Y
Gender	Pearson Correlation	1	.021	.179*	.116	-.037	.040	.091	.140	-.020	.025
Sig. (2-tailed)		.800	.029	.156	.654	.627	.267	.087	.811	.764
N	150	150	150	150	150	150	150	150	150	150
From numbers of years participants working with fashion industry	Pearson Correlation	.021	1	.110	.260**	.142	.186*	.119	.152	.111	.208*
Sig. (2-tailed)	.800		.179	.001	.083	.023	.145	.064	.174	.011
N	150	150	150	150	150	150	150	150	150	150
Is participant’s target Gen Z and Gen Y majorly	Pearson Correlation	.179*	.110	1	.393**	.235**	.468**	.275**	.225**	.193*	.227**
Sig. (2-tailed)	.029	.179		.000	.004	.000	.001	.006	.018	.005
N	150	150	150	150	150	150	150	150	150	150
Big data make easier to collect large volume of data	Pearson Correlation	.116	.260**	.393**	1	.169*	.470**	.256**	.221**	.174*	.178*
Sig. (2-tailed)	.156	.001	.000		.039	.000	.002	.007	.034	.030
N	150	150	150	150	150	150	150	150	150	150
Use of big data by fashion industry increasing customer base	Pearson Correlation	-.037	.142	.235**	.169*	1	.214**	.572**	.484**	.485**	.429**
Sig. (2-tailed)	.654	.083	.004	.039		.009	.000	.000	.000	.000
N	150	150	150	150	150	150	150	150	150	150
Participants using social media for targeting customers towards their product	Pearson Correlation	.040	.186*	.468**	.470**	.214**	1	.231**	.247**	.258**	.250**
Sig. (2-tailed)	.627	.023	.000	.000	.009		.004	.002	.001	.002
N	150	150	150	150	150	150	150	150	150	150
Big data in social media marketing influence Gen Z and Y behaviour	Pearson Correlation	.091	.119	.275**	.256**	.572**	.231**	1	.502**	.610**	.548**
Sig. (2-tailed)	.267	.145	.001	.002	.000	.004		.000	.000	.000
N	150	150	150	150	150	150	150	150	150	150
Use of big data has impact on improving fashion sustainability	Pearson Correlation	.140	.152	.225**	.221**	.484**	.247**	.502**	1	.551**	.657**
Sig. (2-tailed)	.087	.064	.006	.007	.000	.002	.000		.000	.000
N	150	150	150	150	150	150	150	150	150	150
Social media marketing helps achieving competitive advantage and promotes sustainability	Pearson Correlation	-.020	.111	.193*	.174*	.485**	.258**	.610**	.551**	1	.491**
Sig. (2-tailed)	.811	.174	.018	.034	.000	.001	.000	.000		.000
N	150	150	150	150	150	150	150	150	150	150
Sustainable fashion influence buying behaviour of Gen Z and Y	Pearson Correlation	.025	.208*	.227**	.178*	.429**	.250**	.548**	.657**	.491**	1
Sig. (2-tailed)	.764	.011	.005	.030	.000	.002	.000	.000	.000
N	150	150	150	150	150	150	150	150	150	150
*. Correlation is significant at the 0.05 level (2-tailed).
**. Correlation is significant at the 0.01 level (2-tailed).

The above table 11 is presenting Pearson Correlation which is significantly testing all the questions with each other in order to identify the relationship between the dependent and interdependent variable. In respect with the study, the first hypothesis is follows below: 

H1_a: A big data in social media marketing influence consumer buying behaviour of Gen Z and Y in fashion industry 

H1_b: A big data in social media marketing does not influence consumer buying behaviour of Gen Z and Y in fashion industry 

In accordance with correlation analysis table (11), statement 5 and 7 has been correlated:

Statement 5: Use of big data by fashion industry increasing customer base

Statement 7: Big data in social media marketing influence Gen Z and Y behaviour

According to this, the value of r = 0. 572 on the basis of n = 150. In this, the value of p = 0.00. In addition, the coefficient value is lying between ± 0.50 and ± 1, which shows that p < 0.005. It shows that there is a significant relationship between the variables. Therefore, the big data in social media marketing has a positive relationship with the consumer buying behaviour of Gen Z and Y in fashion industry. In this, H1_a is accepted and H1_b is rejected. 

H2: Big data in social media marketing has a significant impact on the fashion sustainability

In accordance with correlation analysis table (11), statement 8 and 9 has been correlated:

Statement 8: Use of big data has impact on improving fashion sustainability

Statement 9: Social media marketing helps achieving competitive advantage and promotes sustainability

According to this, the value of r = 0.551 on the basis of n = 150. In this, the value of p = 0.00. In addition, the coefficient value is lying between ± 0.50 and ± 1, which shows that p < 0.005. It shows that there is a positive relationship between the variables. Therefore, the big data in social media marketing has strong impact on the fashion sustainability. Hence, H2 is accepted.

H3_a: A fashion sustainability influence consumer buying behaviour of Gen Z and Y in fashion industry 

H3_b: A fashion sustainability does not influence consumer buying behaviour of Gen Z and Y in fashion industry

In accordance with correlation analysis table (11), statement 8 and 10 has been correlated:

Statement 8: Use of big data has impact on improving fashion sustainability

Statement 10: Sustainable fashion influence buying behaviour of Gen Z and Y

According to this, the value of r = 0.657 on the basis of n = 150. In this, the value of p = 0.00. In addition, the coefficient value is lying between ± 0.50 and ± 1, which shows that p < 0.005. It shows that there is a significant relationship between the variables. Therefore, it proves that the fashion sustainability transparency has a significant relationship with the consumer buying behaviour of Gen Z and Y in fashion industry. In this, H3_a is accepted and H3_b is rejected.

Regression Analysis

H1_a: A big data in social media marketing influence consumer buying behaviour of Gen Z and Y in fashion industry

H1_b: A big data in social media marketing does not influence consumer buying behaviour of Gen Z and Y in fashion industry

Table 12: Variables (1)

Variables Entered/Removeda
Model	Variables Entered	Variables Removed	Method
1	Big data in social media marketing influence Gen Z and Y behaviourb	.	Enter
a. Dependent Variable: Participants using social media for targeting customers towards their product
b. All requested variables entered.

Below variables has been tested in this study:

Dependent variable: Big data in social media marketing  

Independent variable: Consumer buying behaviour of Gen Z and Gen Y

Table 13: Model Summary (1)

Model Summary
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.231a	.053	.047	.685
a. Predictors: (Constant), Big data in social media marketing influence Gen Z and Y behaviour

Considering the above table 13 (model summary), it has been measured that there is a significant relationship between dependent and independent variable as the value of r = 0.231 and value of R square is 0.53 which is 53%. It shows that the independent variable is 53% dependent on the dependent variable. Additionally, the entire model summary indicates that there is a significant relationship between both the variables and their relationship is positive, which means a strong relationship.

Table 14: ANOVA (1)

ANOVAa
Model	Sum of Squares	df	Mean Square	F	Sig.
1	Regression	3.905	1	3.905	8.324	.004b
Residual	69.428	148	.469
Total	73.333	149
a. Dependent Variable: Participants using social media for targeting customers towards their product
b. Predictors: (Constant), Big data in social media marketing influence Gen Z and Y behaviour

As per the above table 14, the F-ratio in the ANOVA tests in the regression model is fitting with the data i.e., F (148, 149) = 8.324. In this, the significant score is 0.004 and it is lower than the standard rate i.e. 0.05. As 0.004 < 0.005; hence, there is a significant relation between both the variables and regression model is a good fit of the data. In other words, big data in social media marketing has a significant relationship with the consumer buying behaviour of Gen Z and Y in fashion industry.

Table 15: Coefficients (1)

Coefficientsa
Model	Unstandardized Coefficients	Standardized Coefficients	t	Sig.	95.0% Confidence Interval for B
B	Std. Error	Beta	Lower Bound	Upper Bound
1	(Constant)	1.157	.121		9.548	.000	.917	1.396
Big data in social media marketing influence Gen Z and Y behaviour	.147	.051	.231	2.885	.004	.046	.248
a. Dependent Variable: Participants using social media for targeting customers towards their product

According to the table 15, coefficients has been demonstrated in which the value of significant is 0.00 which shows that the independent variable coefficients are different on the basis of statistically significant with 0 (zero). However, the B₀ is tested significant statistically which is an interesting outcome. Moreover, the value of p = 0.00 which is less than 0.005. Therefore, the entire analysis of regression model shows that there is a significant relationship between the variables which proves that big data in social media marketing has a significant relationship with the consumer buying behaviour of Gen Z and Y in fashion industry.

H3_a: A fashion sustainability influence consumer buying behaviour of Gen Z and Y in fashion industry 

H3_b: A fashion sustainability does not influence consumer buying behaviour of Gen Z and Y in fashion industry

Table 16: Variables (2)

Variables Entered/Removeda
Model	Variables Entered	Variables Removed	Method
1	Sustainable fashion influence buying behaviour of Gen Z and Yb	.	Enter
a. Dependent Variable: Social media marketing helps achieving competitive advantage and promotes sustainability
b. All requested variables entered.

Below ariables has been tested in this study:

Dependent variable: Fashion sustainability  

Independent variable: Consumer buying behaviour of Gen Z and Gen Y

Table 17: Model Summary (2)

Model Summary
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.491a	.241	.236	.934
a. Predictors: (Constant), Sustainable fashion influence buying behaviour of Gen Z and Y

Considering the above table 17 (model summary), it has been measured that there is a significant relationship between dependent and independent variable as the value of r = 0.491 and value of R square is 0.241.

Table 18: ANOVA (2

ANOVAa
Model	Sum of Squares	df	Mean Square	F	Sig.
1	Regression	41.017	1	41.017	46.993	.000b
Residual	129.177	148	.873
Total	170.193	149
a. Dependent Variable: Social media marketing helps achieving competitive advantage and promotes sustainability
b. Predictors: (Constant), Sustainable fashion influence buying behaviour of Gen Z and Y

As per the above table 18, the F-ratio in the ANOVA tests in the regression model is fitting with the data i.e., F (148, 149) = 46.993. In this, the significant score is 0.00 and it is lower than the standard rate i.e. 0.05. As 0.00 < 0.005; hence, there is a significant relation between both the variables and regression model is a good fit of the data. In other words, fashion sustainability transparency has a significant relationship with the consumer buying behaviour of Gen Z and Y in fashion industry.

Table 19: Coefficients (2)

Coefficientsa
Model	Unstandardized Coefficients	Standardized Coefficients	t	Sig.	95.0% Confidence Interval for B
B	Std. Error	Beta	Lower Bound	Upper Bound
1	(Constant)	1.081	.164		6.601	.000	.757	1.404
Sustainable fashion influence buying behaviour of Gen Z and Y	.460	.067	.491	6.855	.000	.327	.592
a. Dependent Variable: Social media marketing helps achieving competitive advantage and promotes sustainability

According to the table 19, coefficients has been demonstrated in which the value of significant is 0.00 which shows that the independent variable coefficients are different on the basis of statistically significant with 0 (zero). However, the B₀ is tested significant statistically which is an interesting outcome. Moreover, the value of p = 0.00 which is less than 0.005. Therefore, the entire analysis of regression model shows that there is a significant relationship amongst the variables which proves that the fashion sustainability transparency has a significant relationship with the consumer buying behaviour of Gen Z and Y in fashion industry

Chapter V: Summary, Implications, Conclusions (Discussion)

This chapter is effectively presenting a summary of the overall results which has been gathered and analysed in the study. 5^th chapter involves the conclusion of the study on the basis of the findings. This study also provides some recommendations for future study in the implications. Other than this, the major part of this study is discussion of the results which includes the supportive literatures in the form of evidence and it will address the research question of the study while meeting the aims and objectives.

The study revealed that as per 53.3% of participants big data make it easier for businesses to collect large volumes of client data. Moreover, 32% are strongly agreeing and 30.7% are agreeing with the statement that the use of big data by fashion industry is increasing their customer base. Other than this, as per majority of 65.3%, it has been observed that using social media for targeting potential customers towards your fashion products. Additionally, the results of the responses shows that 72.7% are strongly agreeing or agreeing with the statement that big data in social media marketing influence consumer buying behaviour of Gen Z and Y in fashion industry.

Furthermore, 67.3% of the participants responded that the use of big data has an impact on improving fashion sustainability according to the survey results. The responses also shows that 34.7% are strongly agreeing and 38.7% are agreeing with the statement that social media marketing helps in achieving competitive advantages and promotes sustainability in fashion brand sector. In addition, the outcome of the study similarly reveals that 66% of the participants believes that the sustainable fashion transparency influence consumer buying behaviour of Gen Z and Y. Other than this, moving towards the correlation analysis, the results shows that H1_a is accepted and H1_b is rejected which means there is a positive and strong relationship amongst the variables. Therefore, the big data in social media marketing has a positive relationship with the consumer buying behaviour of Gen Z and Y in fashion industry. Additionally, H2 is also accepted as per the value of r = 0.551 on the basis of n = 150. In this, the value of p = 0.00. In addition, the coefficient value is lying between ± 0.50 and ± 1, which shows that p < 0.005. It shows that there is a positive and strong relationship amongst variables. Therefore, the big data in social media marketing has strong and significant impact on the fashion sustainability. Furthermore, H3_a is accepted and H3_b is rejected which means it proves that the fashion sustainability transparency has a significant relationship with the consumer buying behaviour of Gen Z and Y in fashion industry.

Consequently, the results of regression model reveals that there is a positive and strong relationship amongst the variables which proves that big data in social media marketing has a significant relationship with the consumer buying behaviour of Gen Z and Y in fashion industry in accordance with the ANOVA test and Coefficient variables. Similarly, there is a significant relationship between the variables which proves that the fashion sustainability transparency has a significant relationship with the consumer buying behaviour of Gen Z and Y in fashion industry, in accordance with the ANOVA test and Coefficient variables in the regression model.

The findings of the survey responses reveals that there are 58.7% of the males and 41.3% of the participants are female who are participated in this study. In this, 36.7% were having less than 3 years of experience with the fashion industry, 32.7% of them were having experience between 3 and 5 years, 20.7% of the participants are having 6 to 8 years, and 10% are of more than 8 years of experience. These participants responded that Gen Z and Gen Y are their targeted population as most of them were agreed with the statement, that is 53.3% who believes that their targeted population being Gen Z and Gen Y majorly.

Other than this, the study revealed that as per 53.3% of participants big data make it easier for businesses to collect large volumes of client data. In regards with Muhammad, Dey, and Weerakkody (2018) it has been examined that big data make it easier for businesses to gather bulky volume of data in relevance with the potential clients. Moreover, 32% are strongly agreeing and 30.7% are agreeing with the statement that the use of big data by fashion industry is increasing their customer base. Furthermore, Du et al. (2018) shows that big data contributes in examining the potential customers for the organisation in relevance with the particular product. In respect with the study of Ladhari, Gonthier, and Lajante (2019); Dabija, Bejan, and Dinu (2019), it has been observed that Gen Z and Y are the most potential customers for fashion products. With the use of social media tools, it is easy to identify the targeted customers.

In addition, the responses of participants show that using social media for targeting potential customers towards your fashion products as per 65.3% participants and the results of the responses shows that 72.7% are strongly agreeing or agreeing with the statement that big data in social media marketing influence consumer buying behaviour of Gen Z and Y in fashion industry. Concerning the results, the study of Djafarova and Bowes (2021) examined that the tools of Instagram marketing are found as most effective for influencing the buying behaviour of Gen Z in the fashion industry. In addition, the study of Esteban-Santos et al. (2018) reveals that fashion bloggers have a significant and positive relationship with the consumer buying behaviour of Millennials. It shows, that yes, using social media for targeting potential customers towards your fashion products and big data in social media marketing influence consumer buying behaviour of Gen Z and Y in fashion industry.

Furthermore, 67.3% of the participants responded that the use of big data has an impact on improving fashion sustainability according to the survey results. The responses also shows that 34.7% are strongly agreeing and 38.7% are agreeing with the statement that social media marketing helps in achieving competitive advantages and promotes sustainability in fashion brand sector. In addition, the outcome of the study similarly reveals that 66% of the participants believes that the sustainable fashion transparency influence consumer buying behaviour of Gen Z and Y. Gazzola et al. (2020) had conducted a study on trends in the fashion industry. The results of the study shows that Gen Z and Y are potential customers for fashion industry as they prefer trending collection which helps in increasing the organisational sustainability as well. Moreover, Sun and Wang (2019) highlighted that sustainable social media advertising will be critical in supporting consumers in purchasing items from firms that adopt corporate sustainability methods. As a result, using social networks to reach out to customers on a personalized and engaged level is an important social media advertising technique. The overall discussion shows that the use of social media marketing along with big data practice helps in increasing future sustainability of fashion organisation; however, it influences the behaviour of customers as they prefer popular and influencing brands.

Furthermore, considering correlation as well as regression analysis, the results shows that H1_a is accepted and H1_b is rejected which means there is a positive and strong relationship amongst the variables. Therefore, the big data in social media marketing has a positive relationship with the consumer buying behaviour of Gen Z and Y in fashion industry. Additionally, H2 is also accepted as per the value of r = 0.551 on the basis of n = 150. It shows that there is a positive and strong relationship amongst the variables. Therefore, the big data in social media marketing has strong and significant impact on the fashion sustainability. Furthermore, H3_a is accepted and H3_b is rejected which means it proves that the fashion sustainability transparency has a significant relationship with the consumer buying behaviour of Gen Z and Y in fashion industry. 

According to this study, the findings according with descriptive statistical, correlation, and regression analysis reveals that there is a significant relationship between the variables which proves that big data in social media marketing has a significant relationship with the consumer buying behaviour of Gen Z and Y in fashion industry. In addition, the big data in social media marketing has strong and significant impact on the fashion sustainability. Furthermore, it also has been identified that the fashion sustainability transparency has a significant relationship with the consumer buying behaviour of Gen Z and Y in fashion industry. Based on this it is recommended that the social media marketers can use big data for reaching their customer base. Especially small and medium enterprises are the ones or the new businesses who has to increase their customer reach and influence targeted audience towards their business; for them, using big data is an effective choice. At the same time, these businesses can use social media marketing strategies as it helps in achieving competitive advantages and increases the organisational performance. The use of social media marketing tools is beneficial for collecting a large volume of data of clients. Some companies are transparent about their data gathering tactics, whereas others continue to take their customers in the obscurity. Other than this, consumers are also more concerned about the risk of their data being resold. Though it may be simpler for businesses to benefit in the immediate term, it will weaken customer loyalty and behaviour in the long run.

Other than this, it has been observed that Gen Z and Y are the most potential customers for fashion products. With the use of social media tools, it is easy to identify the targeted customers. Therefore, the study suggests that the fashion organisations those are newly opening their business or running small and medium businesses can use this technique to target these particular group of generation in order to reach potential clients for the products.

These recommendations are adequate for the use of social media managers and other staff member those are using social media for influencing consumer buying behaviour. They can effectively use big data and improve their organisational sustainability.

Conclusion

On the basis of the findings of this study, the study concludes that there is a positive and strong impact of big data (social media) and fashion sustainability transparency on the buying behaviour of Gen Y and Gen Z in the fashion industry. This study has been analysed with the help of collecting responses from the social media managers, administrators, and employees those are working in the fashion industry and uses big data practice for social media marketing. They adequately supported the study to provide a deep understanding about the research problem.

In accordance with answering the research question of this study, the study summarises that big data plays a significant role in increasing the customer base and this practice is done with the help of social media marketing. The study identifies that use of social media marketing is the foremost major for gathering a big data for fashion industry and use it for targeting the potential customers – considering the responses of the participants. In addition, it helps in influencing the behaviour of Gen Y and Z as they highly follow trend and focuses on adhering the influencers. Besides, fashion sustainability influences the behaviour of consumers as they focus on the brands those are sustaining and highly popular. Therefore, is adequately shows that the role of big data (social media) and fashion sustainability transparency is significant on the buying behaviour of Gen Y and Gen Z in the fashion industry.

References

Armat, M.R., Assarroudi, A., Rad, M., Sharifi, H. and Heydari, A., 2018. Inductive and deductive: Ambiguous labels in qualitative content analysis. The Qualitative Report, 23(1), pp.219-221.

Berndt, A.E., 2020. Sampling methods. Journal of Human Lactation, 36(2), pp.224-226.

Black, A., 2010. Gen Y: Who they are and how they learn. Educational Horizons, 88(2), pp.92-101.

Bryman, A. and Bell, E. (2015). Business Research Methods, 4th Ed., Oxford University Press. Chu, S.C., Chen, H.T. and Gan, C., 2020. Consumers’ engagement with corporate social responsibility (CSR) communication in social media: Evidence from China and the United States. Journal of Business Research, 110, pp.260-271.

Chatterjee, S. and Kar, A.K., 2020. Why do small and medium enterprises use social media marketing and what is the impact: Empirical insights from India. International Journal of Information Management, 53, p.102103.

Dabija, D.C., Bejan, B.M. and Dinu, V., 2019. How sustainability oriented is Generation Z in retail? A literature review. Transformations in Business & Economics, 18(2).

Djafarova, E. and Bowes, T., 2021. ‘Instagram made Me buy it’: Generation Z impulse purchases in fashion industry. Journal of Retailing and Consumer Services, 59, p.102345.

Du, M., Wang, K., Chen, Y., Wang, X. and Sun, Y., 2018. Big data privacy preserving in multi-access edge computing for heterogeneous Internet of Things. IEEE Communications Magazine, 56(8), pp.62-67.

Duggan, M., Ellison, N.B., Lampe, C., Lenhart, A. and Madden, M., 2015. Social media update 2014. Pew research center, 19, pp.1-2.

Dwivedi, Y.K., Ismagilova, E., Hughes, D.L., Carlson, J., Filieri, R., Jacobson, J., Jain, V., Karjaluoto, H., Kefi, H., Krishen, A.S. and Kumar, V., 2020. Setting the future of digital and social media marketing research: Perspectives and research propositions. International Journal of Information Management, p.102168.

Egels-Zandén, N. and Hansson, N., 2016. Supply chain transparency as a consumer or corporate tool: The case of Nudie Jeans Co. Journal of Consumer Policy, 39(4), pp.377-395.

Enginkaya, E. and Y?lmaz, H., 2021. What drives consumers to interact with brands through social media? A motivation scale development study. [online] www.sciencedirect.com. Available at: <https://core.ac.uk/download/pdf/82244459.pdf> [Accessed 31 March 2021].

Esteban-Santos, L., Medina, I.G., Carey, L. and Bellido-Pérez, E., 2018. Fashion bloggers: communication tools for the fashion industry. Journal of Fashion Marketing and Management: An International Journal.

Forbath, M. and Schoop, A., 2018. Customer Data: Designing for Transparency and Trust. [online] Harvard Business Review. Available at: <https://hbr.org/2015/05/customer-data-designing-for-transparency-and-trust> [Accessed 30 March 2021].

Foscht, T., Lin, Y. and Eisingerich, A.B., 2018. Blinds up or down? The influence of transparency, future orientation, and CSR on sustainable and responsible behavior. European Journal of Marketing.

Fuchs, C., 2018. Social media, big data, and critical marketing. In The Routledge Companion to Critical Marketing (pp. 467-481). Routledge.

Gardner, T.A., Benzie, M., Börner, J., Dawkins, E., Fick, S., Garrett, R., Godar, J., Grimard, A., Lake, S., Larsen, R.K. and Mardas, N., 2019. Transparency and sustainability in global commodity supply chains. World Development, 121, pp.163-177.

Gazzola, P., Pavione, E., Pezzetti, R. and Grechi, D., 2020. Trends in the fashion industry. The perception of sustainability and circular economy: A gender/generation quantitative approach. Sustainability, 12(7), p.2809.

George, G., Haas, M.R. and Pentland, A., 2014. Big data and management. Academy of management Journal, 57(2), pp.321-326.

Ghani, N.A., Hamid, S., Hashem, I.A.T. and Ahmed, E., 2019. Social media big data analytics: A survey. Computers in Human Behavior, 101, pp.417-428.

Giampietri, E., Verneau, F., Del Giudice, T., Carfora, V. and Finco, A., 2018. A Theory of Planned behaviour perspective for investigating the role of trust in consumer purchasing decision related to short food supply chains. Food Quality and Preference, 64, pp.160-166.

Goertzen, M.J., 2017. Introduction to quantitative research and data. Library Technology Reports, 53(4), pp.12-18.

Huber, B., Barnidge, M., Gil de Zúñiga, H. and Liu, J., 2019. Fostering public trust in science: The role of social media. Public Understanding of Science, 28(7), pp.759-777.

Jacobson, J., Gruzd, A. and Hernández-García, Á., 2020. Social media marketing: Who is watching the watchers?. Journal of Retailing and Consumer Services, 53, p.101774.

Jain, P., Gyanchandani, M. and Khare, N., 2019. Enhanced secured map reduce layer for big data privacy and security. Journal of Big Data, 6(1), pp.1-17.

Jakku, E., Taylor, B., Fleming, A., Mason, C., Fielke, S., Sounness, C. and Thorburn, P., 2019. “If they don’t tell us what they do with it, why would we trust them?” Trust, transparency and benefit-sharing in Smart Farming. NJAS-Wageningen Journal of Life Sciences, 90, p.100285.

James, A.M. and Montgomery, B., 2017. Engaging the fashion consumer in a transparent business model. International Journal of Fashion Design, Technology and Education, 10(3), pp.287-299.

Kapoor, K.K., Tamilmani, K., Rana, N.P., Patil, P., Dwivedi, Y.K. and Nerur, S., 2018. Advances in social media research: Past, present and future. Information Systems Frontiers, 20(3), pp.531-558.

Kollat, J. and Farache, F., 2017. Achieving consumer trust on Twitter via CSR communication. Journal of Consumer Marketing.

Ladhari, R., Gonthier, J. and Lajante, M., 2019. Generation Y and online fashion shopping: Orientations and profiles. Journal of retailing and Consumer Services, 48, pp.113-121.

Ladhari, R., Gonthier, J. and Lajante, M., 2019. Generation Y and online fashion shopping: Orientations and profiles. Journal of retailing and Consumer Services, 48, pp.113-121.

Lee, Y.C., 2017. Corporate sustainable development and marketing communications on social media: Fortune 500 enterprises. Business Strategy and the Environment, 26(5), pp.569-583.

Li, F., Larimo, J. and Leonidou, L.C., 2021. Social media marketing strategy: definition, conceptualization, taxonomy, validation, and future agenda. Journal of the Academy of Marketing Science, 49(1), pp.51-70.

Liu, X., Shin, H. and Burns, A.C., 2021. Examining the impact of luxury brand’s social media marketing on customer engagement?: Using big data analytics and natural language processing. Journal of Business Research, 125, pp.815-826.

Manetti, G. and Bellucci, M., 2016. The use of social media for engaging stakeholders in sustainability reporting. Accounting, Auditing & Accountability Journal.

Muhammad, S.S., Dey, B.L. and Weerakkody, V., 2018. Analysis of factors that influence customers’ willingness to leave big data digital footprints on social media: A systematic review of literature. Information Systems Frontiers, 20(3), pp.559-576.

Naeem, M., 2021. Do social media platforms develop consumer panic buying during the fear of Covid-19 pandemic. Journal of Retailing and Consumer Services, 58, p.102226.

Pandey, P. and Pandey, M.M., 2021. Research Methodology Tools and Techniques.

Pappas, N., 2016. Marketing strategies, perceived risks, and consumer trust in online buying behaviour. Journal of retailing and consumer services, 29, pp.92-103.

Prasad, S., Gupta, I.C. and Totala, N.K., 2017. Social media usage, electronic word of mouth and purchase-decision involvement. Asia-Pacific Journal of Business Administration.

Qin, Y.S., 2020. Fostering brand–consumer interactions in social media: the role of social media uses and gratifications. Journal of Research in Interactive Marketing.

Ragab, M.A. and Arisha, A., 2018. Research methodology in business: A starter’s guide. Management and Organizational Studies, 5(1), pp.1-14.

Ryan, G., 2018. Introduction to positivism, interpretivism and critical theory. Nurse researcher, 25(4), pp.41-49.

Sivarajah, U., Irani, Z., Gupta, S. and Mahroof, K., 2020. Role of big data and social media analytics for business to business sustainability: A participatory web context. Industrial Marketing Management, 86, pp.163-179.

Sladek, S. and Grabinger, A., 2014. Gen Z. Introducing the first Generation of the 21st Century Available at.

Song, C. and Lee, J., 2016. Citizens’ use of social media in government, perceived transparency, and trust in government. Public Performance & Management Review, 39(2), pp.430-453.

Sorensen, K. and Johnson Jorgensen, J., 2019. Millennial perceptions of fast fashion and second-hand clothing: an exploration of clothing preferences using Q methodology. Social Sciences, 8(9), p.244.

Sun, Y. and Wang, S., 2019. Understanding consumers’ intentions to purchase green products in the social media marketing context. Asia Pacific Journal of Marketing and Logistics.

Taguchi, N., 2018. Description and explanation of pragmatic development: Quantitative, qualitative, and mixed methods research. System, 75, pp.23-32.

Tsai, W.H.S. and Men, L.R., 2017. Consumer engagement with brands on social network sites: A cross-cultural comparison of China and the USA. Journal of Marketing Communications, 23(1), pp.2-21.

Venkatesh, V.G., Kang, K., Wang, B., Zhong, R.Y. and Zhang, A., 2020. System architecture for blockchain based transparency of supply chain social sustainability. Robotics and Computer-Integrated Manufacturing, 63, p.101896.

Wilson, E. and Fox, A., 2013. Data collection. School-based Research: A guide for education students, 2, pp.104-125.

Wróblewski, ?., Bili?ska-Reformat, K. and Grzesiak, M., 2018. Sustainable activity of cultural service consumers of social media users—Influence on the brand capital of cultural institutions. Sustainability, 10(11), p.3986.

Zhao, L., Lee, S.H. and Copeland, L.R., 2019. Social media and Chinese consumers’ environmentally sustainable apparel purchase intentions. Asia Pacific Journal of Marketing and Logistics.

Žukauskas, P., Vveinhardt, J. and Andriukaitien?, R., 2018. Philosophy and paradigm of scientific research. Management Culture and Corporate Social Responsibility, 121.