Shankar Subramanian Iyer

Dr A Seetharaman

Dr K Maddulety

S P Researcher

Director, S P Jain

Education transformation using Block Chain Technology-a Student Centric Model

Keywords: Block Chain Technology, Smart Education, Education Transformation, Education, Education Model, Modern Technologies

The Education sector is undergoing transformation-using technology. The virtual classrooms are replacing the traditional classrooms. The proposed model envisaged is student-centric, where the student has the choice to model his curriculum depending on the student interests and area to work and not follow the usual model, using credits from micro-credentials added per unit. The challenge is to use the features, benefits of Blockchain to introduce this new Education model technology to improve efficiency by reducing cost and improving accountability. The transformation in Education Framework can revolutionize the future Learning, teaching Industry to reduce cost and time. This revolution will also lead to improved chances for Learners to be employable. The Research paper proposes to use the Modified ADKAR Change Management Model to validate this Research study and will be a significant contribution to the research topic and the Theory known.

1

Introduction:

The current existing Education System does not satisfy the aspirations of the Learners as is evident from the deteriorating employment rates of the majority of the Countries [START_REF] Peugny | The decline in middle-skilled employment in 12 European countries: New evidence for job polarisation[END_REF]; [START_REF] Taylor | Where do profits and jobs come from? Employment and distribution in the US economy[END_REF]. The fees charged are most Universities so high the Learners cannot afford it. Secondly, even the University Degree does not guarantee jobs except to just a small fraction of the intake. The skills imparted to the Learners are not good enough for the industry, and most of the corporates need to impart training to the recruits. The skills imparted has been discussed by most credible Magazines like Forbes, Gartner and Education experts.

1.1 The transformation of the current Education model to the Student-centric education model is happening daily. The dissent from the Learners on the current courses, which is not giving employment guarantees and the increased cost of studies. The traditional Education model has pressurized the parents, the learners, the bankers and society due to the unemployment rate increases across the globe. Hence, adapting the Customer-centric model, which focusses on the Customer, the students and their needs is a necessity [START_REF] Grech | Blockchain in Education[END_REF]. The main point is that the current education system is not student-centric. Still, the actual position is "Current education system is not very useful to create better employable students for future" (Arnaudov et al., 2015).

1.2 Technologies used for Education are Artificial Intelligence, Virtual reality, Augmented Reality, Machine Learning, Blockchain technology, to name a few. The Technologies and tools have augmented the Classroom Management, Blended Learning, Inclusive Learning to make education to give the Teachers and Learners better experiences. The future years will see more technologies to make Learning better.

Blockchain is a distributed Public Ledger that can automatically record and verify records

which is an essential requirement of Education system where the transcripts, certificates need authentication for the employment procedure. Blockchain can facilitate most of the crucial element for educational institutes. The Education Framework needs most of the advantages of Blockchain Technology except for some challenges that can be modified to be useful [START_REF] Viriyasitavat | Blockchain characteristics and consensus in modern business processes[END_REF]. The An employer to verify the same can view certificates. [START_REF] Crosby | Blockchain technology: Beyond bitcoin[END_REF]. Blockchain technology can improve the way the education system works and improvise the future due to its unique features of decentralization, traceability, immutability, and currency properties; and significant advantages such as reliability, trust, security and efficiency are the needs of the education [START_REF] Jirgensons | Blockchain and the future of digital learning credential assessment and management[END_REF], (Chen, et el 2018).

The need for transformation in the education sector is evident as the current system is not meeting the aspirations of the Learners due to the lack of achieving employability skills. Blockchain Technology analyzing its features, benefits, and challenges can most readily achieve this. Other technologies like VR, ML, IoT, AI can complement these implementations (Ali, 2019). It seems that Technology adaption theory is applicable as new technology like Blockchain needs acceptance, the environment of the Organization and the User perception, count. So, the above theories TAM-TOE is relevant, and the change Management theory ADKAR is relevant. Initially, each of them is inadequate for the current situation of the Blockchain implementation to education.

2

Scope of Research

The main scope in the current situation is the need for a new educational framework, so the focus of this paper will address is followed by the evaluation of Blockchain for suitability to this sector. The research will involve the study of the benefits, features and challenges associated with the Blockchain and match it with the needs of the modern new educational model envisaged. It also involves knowing the lacuna of the current Education system. The paper looks at suggesting an education model which is a student-centric model. It also addresses the benefits and features of using such a model and the challenges, issues the transformation can face. There has been an effort to suggest other areas of work for research.

5

Literature Survey & Review

Existing Higher Education System-Drivers and Barriers

Most Universities aim to enhance student learning through the use of learning technologies in learning, teaching, and assessment strategy, particularly concerning student-centered and flexible learning [START_REF] Davies | Drivers and barriers to the uptake of learning technologies: Staff experiences in a research-led university[END_REF]. The Higher Education system currently driven by commercial interests and not societal needs which have made it vulnerable to profiteering rather than working towards the Learner requirements. Quality has been lately questionable as most University graduates are not able to get jobs to their liking or no jobs at all. The ILO report, the World bank report and UN report have all expressed the concern of high unemployment rates across the globe barring a few areas like Sweden, Norway (ILO report, 2019); (Mac et al., 2016). The barriers are mainly the mindset of people involved, the users, the technology availability and infrastructure.

Proposed Education system features based on Literature-Student Centric Model features-ADKAR

The theory study reveals that the ADKAR Change Management Model, probably with some modification and the TOE is applicable. Conceptual Model of TOE framework adapted for analyzing Technology adoption. The proposed education is inclined to the Student Centric Model, whose features ensure that the learner is the center of the model. The learner decides the curriculum required to go through, the style of delivery, the place and location of study, the technology to be used. The above will make the Learners satisfied with their needs and probably meet their employability needs [START_REF] Aithal | Student Centric Learning through Planned Hard work-An Innovative Model[END_REF].

Technology Influence and Blockchain features-Technology Organization Environment Model

Blockchain is a modern technology which uses decentralized ledgers or parts of the chain called blocks and linked using cryptography. Each transaction of the block recollects the previous one, unauthorized adjustments immediately blocked. The Blockchain works as a decentralized sign up that store records at the Internet with an open get right of entry to the public. Today, education occurs increasingly more out of doors the brick-and-mortar lecture hall universities: it happens on on-line platforms, within communities [START_REF] Tolbatov | Using blockchain technology for E-learning[END_REF].

Conceptual Research Framework and Research Methodology

The

7

Findings, Analysis and Implementation

Details of the current Education Model in Higher Education:

The current Education is more University centric, and not Student Centric. Universities offer preapproved Programs from the Awarding Body, and the Student has to select out of the fixed programs (Khoon et al., 2012). The Learner is not motivated, sometimes not interested in the total program, at the best some parts of the program is useful to the Learner. However, the Student forced to take the full program irrespective of whether it is useful to him or not or get the student employment. It is evident across the global economies, and most education systems are not in a position to reduce unemployment [START_REF] Kromydas | Rethinking higher education and its relationship with social inequalities: past knowledge, present state and future potential[END_REF]. The current Education Model cannot guarantee Jobs nor guarantee full employment [START_REF] Núñez | Higher education and unemployment in Europe: an analysis of the academic subject and national effects[END_REF]; (ILO report, 2019).

This disruption is happening due to several factors coming together: the ever-rising college fees, which is not sustainable nor affordable for the Learners [START_REF] Khalid | Promising digital university: a pivotal need for higher education transformation[END_REF]. A change in Learner expectation, needs and mindset, declining consumer demand among prospective students, extreme lack of the work readiness of fresh college graduates, a rhetoric of what makes college useful (workplace-integrated and team relationship), and emerging talent attraction and development strategies by employers (Brandon Busteed, 2019).

The changing Education Scenario

In the Student Centric Education Model mentioned above, the student can choose the course curriculum, the University, the Awarding Body, the type of classroom, delivery type, the course H1 H2 H3 H4 Change Management model ADKAR Model can be applied here as it discusses the details of change management to be adapted. Once the implementation is started or set in place, it needs to be monitored closely [START_REF] Tang | Change management[END_REF]. The TOE Theory of Environment also applies as the Educational Environment and Organization. The Change Management -ADKAR Change Management Model will be validated statistically using Qualitative data collection, survey Questionnaire. It involves testing and evaluating of the Structural Equation Model on Adanco, testing the correlation.

The suggested Needs of the student-centric education model in Current Scenario

The Learner centric Learning focuses on flexibility in the choice of subjects to study, select their curriculum, identify the best timeline, schedule in completing their program. The student can select the mode of learning like direct learning in classrooms, virtual classroom using technology online. They have the flexibility in selecting the evaluation system and the examination system (Henderson et al., 2019), [START_REF] Seechaliao | Instructional strategies to support creativity and innovation in education[END_REF]. This model can allow Learners to add more credit papers as the course progresses, even more than the minimum requirement. Workplace Learning allows the students to earn while studying, so the burden on the financial need reduced, and the defaults on student loans avoided [START_REF] Fernando | Teaching, learning and evaluation enhancement of information communication technology education in schools through pedagogical and e-learning techniques in the Sri Lankan context[END_REF].The Workforce indulges in more comfortable and faster ways of performing work by adopting technology (Komori, 2018).

The new Learner is impatient and does not want to wait for employment to pay off the loans borrowed for financing these courses. The current Scenario is due to lack of well-paying jobs even after guarantees from the best Colleges across the globe [START_REF] Mikkonen | Guiding workplace learning in vocational education and training: A literature review[END_REF]. The academic units and workplace learning blending happen under the expertise of the Classroom Lecturer and the Workplace Manager [START_REF] Pylväs | Finnish apprenticeship training stakeholders' perceptions of vocational expertise and experiences of workplace learning and guidance[END_REF]; [START_REF] Donald | Students' perceptions of education and employability[END_REF].

How the Block Chain will help implement the New Education Model-Student Centric Approach

The primary requirements of the New Education Model-Student Centric Approach has cost reduction, enhanced efficiency, immediate authentication, transparency, security, faster operation speeds, low cost of processing and decentralized ledgers avoids hacking and loss of data and safeguard of confidentiality of stored data, which can be easily retrieved. All these are the useful features of Blockchain technology [START_REF] Xie | A survey of blockchain technology applied to smart cities: Research issues and challenges[END_REF]; (Msomi el at, 2018); [START_REF] Grech | Blockchain in Education[END_REF].

These are the benefits required for the Education Domain, as this will ensure transactions and information to be secured and time-stamped. It can be then encrypted in codes and stored for further reference. The Certificates authentication happens with the click of the mouse (Chen et al., 2019); [START_REF] Arnaudov | Technology Implementation Success Model Designed for Educational Organizations[END_REF]. The usefulness of Blockchain, Artificial Intelligence, Virtual Learning, Machine Learning, Virtual Reality is quite interesting to study and apply to the Education Domain (Kolekar el at, 2019).

Adanco Model, Findings and Discussions

The quantitative approach is deductive research which utilizes a structured approach, statistics, and large sample size to analyze data. Looking at the sampling methodology, the population of this research is India, UAE, Singapore, Malaysia and other parts of the World. Most of the studies conducted in other countries focused on the town or city of the country; therefore, the UAE being the location of research. Based on sampling methodology, this research uses simple random sampling methodology (Sa'ait et al., 2016; Huyen and Costello, 2017). The data collection process conducted among a population of 20 million who were able to access the Federal Territory of UAE through online distribution. According to Raosoft Inc (2018), the results from Raosoft Sample Size Calculator suggested that 386 is the minimum sample size of the survey (margin of error 5 per cent; confident level 95 per cent). The total number of respondents targeted was 385. The inclusion criterion in respondent selection is that the individual had to be a student, Teacher, the user of Blockchain, Consultant, Educationists. This research uses the five-point Likert scale for the survey statements. Besides, according to Sarstedt et al. ( 2014), PLS-SEM is a useful tool for family business research, and this model widely used in many fields of business research. PLS-SEM was used to measure and evaluate the reflective measurement and structural measurement. There are two stages for this model; stage one is to measure the reflective model (Nimako et al., 2014). In the beginning, indicators reliability, internal consistency reliability, convergent validity, and discriminant validity assessed in a reflective model. It is important also to measure the collinearity, predictive relevance, significance, and relevance of path coefficients in the context to the overall structural measurement of the model.

Data validation and analysis

Respondents' characteristics

Table I shows the profile of respondents who had participated in the online survey. A total of 886 survey questionnaires were distributed to millennials in Federal Territory of UAE, Indian cities, Malaysia and Singapore electronically using emails, WhatsApp and other social media means, but only 386 (43.3 per cent) of respondents participated in this research. In this research, the male respondents were 238 (61.73 per cent), while female respondents were 148 (38.27 per cent). Besides, as this study focused on millennials, the terms of age group were limited and divided into two groups, which were 18-27, 28-36 and 36-44. Based on the results from Table I, the majority of the respondents aged between 18 to 44 years (80 per cent, 308 respondents), while only 81 respondents aged beyond 46 years. As for the nationality, all the respondents were majorly Indian, UAE, Pakistani, Malaysian and Singaporeans, which fulfilled the essential requirement of the research. In terms of education level, majority of the respondents were Postgraduates (182; 47.1 per cent). Respondents in Graduate were 136 (35.3 per cent), Doctorates were 39 (10.1 per cent), and rest were 29 (7.6 per cent). The descriptive statistics showed that the majority of the millennials that participated in this research were undergraduates. Overall Model-all path coefficients have *** indicating all of them are significant and consistent to the SEM. Overall R 2 of 0.641 means that 64.1% of the relations and correlations between the constructs have been explained satisfactorily which is considered good statistically.

Fig. 3Table 2 Goodness of model fit (saturated model)

A cut-off value SMRM of 0.08, as proposed by Hu & Bentler (1999), appears to be better for variance-based SEM and met by this Model. ADANCO 2.0.1 uses bootstrapping to provide the 95%-percentile ("HI95") and the 99%-percentile ("HI99") for the dULS if the theoretical model was true. (If the dULS exceeds these values, it is unlikely that the model is true.) In our results the generated values are within the values of the dULS which indicates that the model is likely to be true as per Dijkstra & Henseler (2015a).

Reflective measurement model

The reflective measurement is the first stage of examining the reliability and validity of the measurement model. This section will be divided into several subtopics: (1) indicator reliability (loadings), (2) construct reliability, (3) convergent validity and, (4) discriminant validity (Sarstedt et al., 2014).

Indicator reliability (loadings)

Examining the indicator loadings was the first step in the reflective measurement, and the loadings score above 0.7 represented that the construct explained more than 50 percent of a variable (Sarstedt et al.,2014). Based on Table 3, the results showed that all the indicators were over 0.7.

Internal consistency reliability

Internal consistency reliability was used to examine the reflective measurement model, where previous studies stated that the higher the value indicator, the higher the degree of reliability (Joseph et al., 2010). Also, Jöreskog's rho (ρc) was the standard method used to evaluate the internal consistency reliability. The minimum acceptable value for internal consistency reliability was 0.6, as the value below was considered as having low significance or weak reliability. The maximum value was considered as 0.95 as the value over it was considered as problematic, or the indicator was redundant (Sarstedt et al., 2014). Based on Table 3, the result showed that the Jöreskog's rho (ρc) values for each construct, that is, People Related Factors (0.9244), Technology related factors (0.9270), Environmental related factors (0.9158), Organizational related factors (0.9243), and Adaption related factors (0.9440), ranged within 0.6 and 0.95. Therefore, all the Jöreskog's rho (ρc) values in this research were considered as "satisfactory to good," and all of the indicators in this research were significant and reliable for the internal consistency reliability.

Fig. 4 Table 3: Indicator Loadings

Construct reliability

For the extent of internal consistency reliability and construct reliability, Cronbach's alpha was used to measure and examine the reliability for all the constructs. The minimum acceptable Cronbach's alpha is 0.7 or above and the higher the Cronbach's alpha, the higher the reliability of multiple measures for the measurement of each construct (Joseph et al., 2010). Based on the results from Table 4, all the constructs were higher than the standard requirement (0.7) with People Related Factors as 0.8927, Technology related factors as 0.8819, Environmental related factors as 0.8843, Organizational related factors 0.8982, and Adaption related factors as 0.9438. Based on the results, the AVE value of People Related Factors with the construct of People Related Factors was 0.6982, the AVE value of technology functionality with the construct of technology functionality was 0.7867, the AVE value of perceived usefulness with the construct of perceived usefulness was 0.7479, the AVE value of perceived ease of use with the construct of perceived ease of use was 0.8285, and the AVE value of adoption intention with the construct of adoption intention was 0.797. Fig. 6. Table 5 Discriminant Validity: Fornell-Larcker Criterion

Structural model assessment

Before the structural model assessment, the potential collinearity between the predictor constructs must be tested to ensure the quality of the results. Therefore, this section will be divided into several subtopics: (1) collinearity, (2) predictive relevance and, (3) significance and relevance of path coefficients.

Collinearity

In order to ensure the results were not biased by collinearity issues, variance inflation factor (VIF) was used to measure for each indicator in the construction. Usually, the value of VIF less than 5 is acceptable and assumed to be safe for avoiding any collinearity issues, but there could be exceptions (Benitez et al., 2020). Based on the results, all the indicators' VIF values were below five which is acceptable. This result represented that there were no significant collinearity issues observed in this model.

Predictive relevance (R 2 )

The coefficient of determination (R 2 ) was used to measure how well the construct was explained toward all the constructs in the research. According to some researchers, the minimum requirement of R 2 was 0.2, and the construct was relevant and significant if the value of R 2 exceeded 0.2 (Hair et al., 2011). Based on the result, the value of R 2 of adoption intention was 0.6414, which represented that the construct was relevant and significant, and considered as moderately high in explaining all the variables in the research.

4. Significance and relevance of path coefficients.

The final step in the structural model assessment was significance and relevance of path coefficients. According to Sarstedt et al. ( 2014), the researchers stated that the standard range for path coefficient values was from -1 to +1 one, while the value closer to +1 represented strong positive relationship, and the value closer to -1 represented strong negative relationship. The results from the bootstrapping procedure (386 cases, 1000 samples, no sign changes option) reveal that all four structural relationships were significant (p ≤0.05). The results showed that all the independent constructs had positive and significant relationship toward Blockchain technology adaption to Education, and the results represented that all of the hypotheses were authenticated and supported by data.

Fig. 7. Table 6 Direct Effects Inference

The results showed that all the independent constructs had a positive and significant relationship with Blockchain Technology's adaption for Education. The results represented that all of the hypotheses were authenticated and supported by the data.

The research objective of this study was to identify the relationship between constructs of technology-related factors, People's related Factors, Environmental related Factors, Organizational related factors and Adaption of Blockchain Technology for Education. All the hypotheses are accepted, and the relationships are statistically established through this study to achieve the primary objective.

Fig.8. PLS-SEM Validation

ADKAR Change Management Model, Transformation Theory, TAM-TOE, individually seem inadequate for the current COVID, economic, technology bound situation. An integrated model using some of these identified constructs with more relevant factors considered seem appropriate. So, the above research framework was developed and tested for validity and reliability using PLS-SEM has been a useful contribution of this research paper and by getting a consensus of 386 respondents-stakeholders of the Blockchain education application. The methodology followed goes a long way in addressing the scarcity of relevant data for future researchers and lay the path for further research by developing on this model or such similar models. The above-cited theories have their importance in a particular situation in stable economies, equal education opportunities, infrastructure availability. However, in recession, COVID, sanction regimes, these above theories seem lacking to explain many factors. Hence a concrete, a sound research-based framework has been developed to contribute to further work.

Contributions

Research Implications

The research study indicates that extending the technology acceptance model along with Change Management model while measuring the User's adoption intention. The validated hybrid model through SEM-PLS provides a better understanding to researchers and practitioners why individuals choose technology for Education Purpose.

Managerial Implications

The technology managers who are investing in BCT for knowledge workers would gain a better understanding of consumer perspective through this study's finding. The hybrid framework allows technology managers to assess BCT adaption for Education through specific contexts such as Student-centric Model.

Adaption of Education (ARF) transformation-student centric Model

Independent Variable

Based on the research data, the adoption intention must not focus on a particular age group or education level of users. This finding provides a strong business case for solution providers in emerging economies to focus on appropriate tools and features to be rolled out using technology.

Limitations

The limitation of this Research can be not enough evidence is registered or discussed on the actual failures of the education system despite the Learners disgruntled with the shortcomings. The need to change to student-centric has not been addressed in a significant way by the various stakeholders. The other limitation being the lack of successful implementations of Blockchain in various applications except for Bitcoin and other crypto Currencies which have some lacuna. So, at best the exposure can be called Smart system which passed off as blockchain technology which it is not as all the participants are not at the same operating level. Second, the volumes of scale do not justify the spends on the BCT as the energy requirements, and initial Investments are enormous to get ROI. The lack of Sponsor Organization and Regulatory body is not able to give the BCT application the credibility. Blockchain technology claims of successful implementation in the education sector tested.

Conclusion

The student-centric education model is the future of Global Education. The Model is going to be powered by modern technologies like Blockchain Technology, Artificial Intelligence, Machine Learning, Virtual Imaging, Virtual Reality. It will reduce the cost, efforts, staff hour needs and security of the new education system. A smart city like Dubai is well poised to take this route due to the availability of the Infrastructure, the political will and the skilled workforce. Education student-centric model in smart cities like Dubai is going to make it the future. The investment and the process are well on its way for this educational transformation. The success of the implementation of the new Model will depend on how Management able to handle these factors properly and control the interplay [START_REF] Dutta | Strategic Change and Transformation: Managing Renewal in Organisations[END_REF].

H1:Fig. 1 .

 1 Fig. 1. Conceptual Framework-Integrated extended ADKAR-TAM-TOE Model.

Fig. 5 .

 5 Fig. 5.Table 4 Construct Reliability

H4

  β= 0.1525, t=6.2108 All path coefficients are significant *** and t-values are above 5, so the model indicates good relationships between the constructs.

  It might not mean that the Learner can choose for their best; however, it leads to their making a choice and not blame others for it. The above model is very much possible using Blockchain Technology. The cost will be very reasonable, as the Universities do not incur the cost of Infrastructure, Lecturers. Universities get more Learners across the globe, and depending on their reputation will get profitable Business to make this Investment viable[START_REF] Aithal | Student Centric Learning through Planned Hard work-An Innovative Model[END_REF]. Most of the Quality institutions should strategize to transform students into life-long learners and innovators by enabling them to think critically, and to encourage them to scientific research (Kennedy et al., 2019).

	material provided on a single Platform.
	People's related Factors (PRF)	Dependent Variable
	-Awareness of need to change in existing
	Education System
	-Desire to Change -Implementation Knowledge of change -Ability to demonstrate skills and behaviors -Reinforce to make the changes to SCM stick	Adaption of Education (ARF) transformation-student centric Model-BCT will benefit Education transformation, Education will adopt BCT for
		transformation, BCT is best
		suited to for Education
		transformation
	Figure 5

Technology Related (TRF)-Modern technologies for this change to SCM (TOE)-Compatibility, complexity, Perceived Barriers, Perceived benefits, Perceived ease of use, Perceived importance, Perceived risks, Relative Advantage, Trialability Environmental Related (ERF) to Implement this change to SCM-Competition, Customer Pressure, Customer readiness, External pressure, Government policies, Market uncertainties, Third Party Organization (ORF)--Training and Supervision, Financial readiness, Firm size, Internal needs, IT Expertise, IT resources, Technical competencies, Technical readiness

Table 1 .

 1 Demographics

	Demographic Variable	Category	Percentage
	Age Group	18-25	24.4
		26-35	32.8
		36-45	21.8
		46-55	14.3
		56+	6.7
	Gender	Male	61.73
		Female	38.27
	Demographic			Demographic		Percent-
		Category	Percentage		Category
	Variable			Variable		age
	Education	Highschool	1.7 (6)	Income Level	Under 5000 AED	28.8
		Undergraduate	5.9 (23)		5001-10000 AED	16.1
		Graduate	35.3 (136)		10001-15000 AED	15.3


Kavisanmathi
email: kavisanmathi.19mba@kct.ac.in

Does Herding behaviour among traders increase during Covid 19 pandemic?: Evidence from the Cryptocurrency market

Keywords: Cryptocurrency, Herding Behavior, CSSD

Cryptocurrencies are digital currencies and trading these currencies have gained huge momentum in recent years. The sophisticated features, complexities on regulatory framework, and high volatility of Cryptocurrencies would pose trading challenges to new investors and/or investors with limited knowledge. Investors generally are influenced by fund managers, financial analysts or other investors who are considered as well informed and highly knowledgeable peers. Investors mimic their behaviour to perform trading activities and such behaviour is termed as Herding. Covid 19 pandemic triggered severe uncertainties in the cryptocurrencies market and has led to wide fluctuations in prices causing severe volatility and market crashes. This paper aims to examine the herding behaviour in cryptocurrency market during the pre Covid 19 and Covid 19 pandemic period using the Cross-Sectional Standard Deviation (CSSD) approach. The findings of the paper reveal that herding was evident among all the ten crypto-currencies in normal market conditions of the entire sample period but not during market upswing or downswing. However, the herding behaviour was present in the cryptocurrencies Litecoin, Cardano and Dash during the Covid 19 pandemic period in all market conditions.

Introduction

Cryptocurrency, a phenomenal financial and technological innovation has become a key investment option for traders in recent years. With the introduction of Bitcoin in the year 2009 and its wide acceptance, an array of other cryptocurrencies was introduced in the market. Cryptocurrencies subsequently launched was tailored to adapt to Bitcoin and other leading cryptocurrencies characteristics while having to demarcation on certain unique characteristics on its own. As of August 5, 2020, top hundred cryptocurrency trading volume was $83 billion (coinmarketcap.com) which indicate an increased participation and trading activity among the investors. Cryptocurrencies does not have fundamental value and hence their prices do not reflect all the information [START_REF] Cheah | Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of Bitcoin[END_REF].

Cryptocurrencies also exhibit exceptional returns coupled with high volatility [START_REF] Bouri | Herding behaviour in cryptocurrencies[END_REF]. Being an under-regulated crypto market with lack of transparency in information, naïve traders and investors may venture into this market without realizing the risks associated on their investment. The novel features, ambiguity in information, high volatilities in the cryptocurrency market may cause the investors to rely on the judgments on financial advisors or fund managers or other investing forum. In financial theories, herding behaviour of investors relate to their trading decisions based on the views and actions on others. Lack of confidence, fear or anxiety in the investors may also lead them to mimic the trading behavior and decisions or other investors or fund managers. This behaviour may have several consequences such as price instability, higher volatility, market bubbles or crashes [START_REF] Hwang | Market stress and herding[END_REF].

The financial markets are susceptible to unprecedented calamities, natural disasters, war, and pandemics leading to market crashes and financial crisis. In the case of pandemics/epidemic crisis, the previous occurrences such as the HIV/AIDS, SARS, Ebola and H1N1 which posed a threat to the lives of individuals had impacted the global financial markets and economy [START_REF] Jamison | Improving Health and Reducing Poverty[END_REF]. However, the severity in case of the recent Covid 19 pandemic is much higher in the financial markets which has disrupted the global economic activity as a whole causing collapse of several global economies and causing global stock market crashes. Yarovaya et al. [START_REF] Yarovaya | The Effects of a 'Black Swan' Event (COVID-19) on Herding Behavior in Cryptocurrency Markets: Evidence from Cryptocurrency USD, EUR, JPY and KRW Markets. EUR, JPY[END_REF] has noted that experts opine that Covid 19 may lead to financial crisis and may be predicted as a "black swan event", an event which is unpredictable and has not occurred earlier. Such events may impose challenges on model predictions and calculate risk assessment measures. Hence, investors trading in cryptocurrencies are confronted with uncertainties due to the Covid 19 pandemic which may lead to herd behaviour. The stock market crash and relevant investor uncertainties causes investors to deviate from their rational behaviour and exhibit biases and herding [START_REF] Calderón | Herding behavior in cryptocurrency markets[END_REF].Overexcitement among investors leading to high volume trading causes herding behaviour in extreme volatile conditions [START_REF] Jalal | Herding Behaviour and Cryptocurrency: Market Asymmetries, Inter -Dependency and Intra-Dependency[END_REF] and the cryptocurrency prices are driven by herding behaviour [START_REF] Poyser | Herding behavior in cryptocurrency markets[END_REF]. Hence, examination of herding behaviour in cryptocurrency market is important during uncertain market conditions. In particular, when market crashed due to Covid 19 pandemic, herding behavioral characteristics could be a potential contributor on cryptocurrencies price variations and trading volume and its associated liquidity. On this context, this study examines the herding behaviour in the cryptocurrency market prior to Covid 19 pandemic and whether this trend has increased during the pandemic period.

Literature Review

Several researchers have tried to explore the dynamics of various market crashes particularly in the equity markets. Presence of herding behaviour in stock markets during the crisis period has been investigated in stock markets [START_REF] Hwang | Market stress and herding[END_REF]. Investors tend to herd or mimic other investors during extreme volatile conditions in the markets hoping to make profits [START_REF] Kallinterakis | Herding and feedback trading: Evidence on their relationship at the macro level[END_REF]. Studies by Lao & Singh [START_REF] Lao | Herding behaviour in the Chinese and Indian stock markets[END_REF], Hammami & Boujelbene [START_REF] Hammami | Investor Herding Behavior and Its Effect on Stock Market Boom-Bust Cycles[END_REF], Jlassi & Naoui [START_REF] Jlassi | Herding behaviour and market dynamic volatility: evidence from the US stock markets[END_REF], Clements et al. [START_REF] Clements | An empirical investigation of herding in the US stock market[END_REF], Demirer et al. [START_REF] Demirer | Herding and flash events: Evidence from the 2010 Flash Crash[END_REF] and Bansal [START_REF] Bansal | Behavioral Finance and COVID-19: Cognitive Errors that Determine the Financial Future[END_REF] have confirmed the herding behaviour during market turbulence or crisis period whereas studies by Ahsan & Sarkar [START_REF] Ahsan | Herding in Dhaka stock exchange[END_REF] have provided evidence of non-existence of herding behaviour as well.

Given the market severity and volatility during crisis period, investigation on herding behavior of investors becomes vital. Sophisticated features of cryptocurrencies [START_REF] Krueckeberg | Cryptocurrencies as an asset class[END_REF] and its extreme volatile nature may cause herding activity among investors particularly during extreme and volatile market conditions. Herding behaviour in cryptocurrency markets were examined during different market conditions. Conrad et al. [START_REF] Conrad | Long-and cryptocurrency volatility components: A GARCH-MIDAS analysis[END_REF] examined herding behaviour in cryptocurrency market using CSSD, Cross Sectional Absolute Deviation of returns (CSAD) and Markov-Switching model, and found that herding existed in extreme market conditions. Silvia et al. [START_REF] Da Gama Silva | Herding behaviour and contagion in the cryptocurrency market[END_REF] studied herding formation using modified CSAD model on select 50 cryptocurrencies based on liquidity and market capitalization. The study revealed that severe herd behaviour was exhibited during extreme periods. Vidal-Thomas et al. [START_REF] Vidal-Tomás | Herding in the cryptocurrency market: CSSD and CSAD approaches[END_REF] examined herding using static, rolling, and logistic regression model and reported that herding activity increased with market uncertainty. Ballis & Drakos [START_REF] Ballis | Testing for herding in the cryptocurrency market[END_REF] employed GARCH models to examine herding behaviour on top five cryptocurrencies, and found herding existed during market up and down periods. Poyser [START_REF] Kallinterakis | Herding and feedback trading: Evidence on their relationship at the macro level[END_REF] used Markov-Switching approach and reported that herding existed during different market regimes. Jalal et al. [START_REF] Poyser | Herding behavior in cryptocurrency markets[END_REF] confirmed the presence of herding behaviour in the extreme conditions of bullish and high volatility market situations of major cryptocurrencies listed in CCI30 index and sub-major cryptocurrencies and major stock returns listed in Dow-Jones Industrial Average Index, from 2015 to 2018. Junior et al. [START_REF] Raimundo Júnior | Market Stress and Herding: A New Approach to the Cryptocurrency Market[END_REF] noted a positive relationship between herding and market stress conditions. The studies on herding behaviour in the cryptocurrency market had established the presence of herding activity among investors during uncertain market conditions.

Covid 19 pandemic has caused uncertainty across the globe leading to falling economies, market crashes, unemployment, and other unexpected uncertainties. Previous pandemics/epidemics including the Spanish flu, SARS, HINI, HIV/AIDS had caused impact on financial markets. But in the case of Covid 19 pandemic, the lockdown restrictions and the halt of several global industrial and economic activities led to a significant impact on the financial markets [START_REF] Baker | The unprecedented stock market reaction to COVID-19[END_REF]. Ma et al. [START_REF] Ma | Global economic and financial effects of 21st century pandemics and epidemics[END_REF] and Baker et al. [START_REF] Baker | Covid-induced economic uncertainty (No. w26983[END_REF] noted severity in economic impact and Eichenbaum et al. [START_REF] Eichenbaum | The Macroeconomics of Epidemics[END_REF] on the huge loss in lives, Under such uncertain economic and high volatile conditions in cryptocurrency markets due to the pandemic, the herding behaviour in investors may tend to increase as established by several researchers. Kizysa et.al., [START_REF] Kizys | From COVID-19 herd immunity to investor herding in international stock markets: The role of government and regulatory restrictions[END_REF] have noted that the Government and regulatory restraints imposed to control the transmission of Covid 19 within and across countries can ease the investor herding behavior in international stock markets. Few studies have examined the herding behaviour in cryptocurrency markets during the Covid 19 pandemic. In case of cryptocurrencies, Yarovaya [START_REF] Calderón | Herding behavior in cryptocurrency markets[END_REF] reported that Covid 19 does not significantly amplify herding in the markets whereas Mnif et al [START_REF] Mnif | How the cryptocurrency market has performed during COVID 19? A multifractal analysis[END_REF] found herding behaviour reduced after Coronovirus outbreak. Most of the studies on cryptocurrency market have tried to capture the herding behaviour in different market conditions. The limited evidences of Yarovaya [START_REF] Calderón | Herding behavior in cryptocurrency markets[END_REF] and Mnif et al. [START_REF] Mnif | How the cryptocurrency market has performed during COVID 19? A multifractal analysis[END_REF]

Methodology

This study aims to examine the herding behaviour in top ten cryptocurrencies. The ten cryptocurrencies used for this study are Bitcoin, Ripple, Ethereum, Bitcoin cash, EOS, Litecoin, Monera, Cardano, IOTA, and Dash. Crypto10 Index. The market index chosen was BITA Crypto10 (B10) Index, provided by the company BITA, a German-based Fintech company was introduced on 20th September 2018. The Crypto10 Index is calculated in US dollars (USD) and calculated on a daily basis. It represents the performance of the 10 largest cryptocurrencies based on the market capitalization. Daily closing prices of the Index and the ten cryptocurrencies (from BITA crpto-10 Index) are sourced from bitadata.com and coinmarketcap.com are used for the analysis. The sample period considered for this study is from July 29, 2019 to July 28, 2020 comprising of the pre Covid 19 and Covid 19 period. The BITA Crypto 10 Index returns are examined for normality and stationarity for usage in subsequent analysis. The best possible approach to test herding is to directly observe the investor's actions. But in cryptocurrencies, it is almost impossible because of its privacy and so different proxies are developed to detect herding behavior of the crypto market based on returns' regression rate. Hence this study employs the methodology adopted by Chang et al. [START_REF] Chang | An examination of herd behavior in equity markets: An international perspective[END_REF] an improvement from the original methodology offered by Christie and Huang [START_REF] Christie | Following the pied piper: Do individual re-turns herd around the market?[END_REF]. Christie and Huang [START_REF] Christie | Following the pied piper: Do individual re-turns herd around the market?[END_REF] suggested the use of Cross-Sectional Standard Deviation of returns (CSSD) to identify herding behavior in financial markets. CSSD = √{((Bitcoin return -Average of B10 return)2) / N-1}

where, Bitcoin return is computed using the formula (Today's closing priceprevious day closing price)/ previous day closing price,B10 return is computed using (Today's index valueprevious day index value)/ previous day index value, and N is the number of days taken for the study. According to Christie and Huang [START_REF] Christie | Following the pied piper: Do individual re-turns herd around the market?[END_REF] herding can be tested when there are upswings and downswings (under market stress events) by exploiting investors' tendency to overturn their private beliefs in favor of the market consensus. The Capital Asset Pricing Model (CAPM) predicts that the dispersion will increase with the absolute value of the market return since individual assets differ in their sensitivity to the market return. And also, if herding exists, individual returns will not differ greatly from the market results. The CSSD refers to the average proximity of individuals' returns to the mean and will always be equal greater than zero. The value in the lowest bound signifies when all the returns converge while a deviation from the zero represents dispersion. The herding behaviour in the cryptocurrencies are studied using Dummy variable Ordinary Least Square (OLS) regression model as shown in equation 2.

Log (CSSDi) = c + dl + du (2)
where, CSSDi is the CSSD of the log return of the individual cryptocurrency, dl is 1 if market return lies on the lowest side of the distribution and 0 otherwise, du is 1 if market return lies on the upper side of the distribution and 0 otherwise. The residuals of the equation are examined for autocorrelation and heteroskedasticity. The residuals of the Dummy variable regression were verified for presence of autocorrelation using Ljung-Box (L-B) portmanteau test and heteroskedasticity using Lagrange's Multiplier (LM) test for ARCH-effect. Where the presence of ARCH effects in the currency returns GARCH (1,1) regression models was used for the analysis.

Analysis and Findings

The time series graphs of the ten cryptocurrencies are illustrated in Figure 1 plotted with prices on Y axis and days on X axis. It can be observed that all the cryptocurrencies witnessed a massive fall in their prices during the months December 2019 and March 2020.The fall in December 2019 prices may be attributed to the event when the outbreak of Coronavirus at Wuhan, China was announced. The subsequent major fall in prices during March 2020 may be due to the nationwide lockdown imposed across several global countries to contain the spread of the virus in their respective countries. The global financial markets were forced to stop trading due to the steep fall in prices and the markets moved to bear trend. It may be inferred that in such market stress situations investors uncertainty and panic would have led them exhibit herding behaviour. CSSD series for longer sample periods may not be normally distributed and regularly tend to show excess kurtosis and skewness which are basic with financial parameter [START_REF] Mcdonald | Distributional characteristics: Just a few more moments[END_REF].

Brooks [START_REF] Brooks | Introductory econometrics for finance[END_REF] recommends the usage of natural logarithmic transformations to meet the normality assumption as it can help to make a skewed distribution closer to a normal distribution. The results of the log CSSD value of the cryptocurrencies carried out using ADF test showed that the t-statistic values were lesser than the critical values -

Herding behaviour during pre-Covid 19

The results of the Dummy variable OLS regression model for the ten cryptocurrencies during pre-Covid 19 periods using Equation 3 are provided in Table 1. The residuals of these models were examined for presence of autocorrelation and heteroskedasticity. The residuals of the Ethereum CSSD regression was found to be autocorrelated and hence AR (1) term was added in the model. The residuals of the Bitcoin CSSD and EOS CSSD regression had ARCH effects and hence GARCH (1,1) model was employed. It can be observed from Table 1, the coefficients of dl in the models for the cryptocurrencies Bitcoin (1.1252), Ethereum (1.2068), Ripple (1.1607), Bitcoin cash (1.3006), EOS (1.1954) were positive and significant. According to Christie and Huang [START_REF] Christie | Following the pied piper: Do individual re-turns herd around the market?[END_REF], a positive and significant coefficient indicates absence of herding behaviour. It can be inferred that during market downswing or when the market returns were negative there was no existence of herding activity. However, for the cryptocurrencies Litecoin (-0.2790), Monera (-0.5218), Cardano (-0.1918), IOTA (-0.4949) and Dash (-0.3103) the coefficients of dl were found to be negative and significant. A statistically significant negative coefficient indicates presence of herding activity in the market downswing period. It can also be inferred that herding activity was not evident for the top five cryptocurrencies during market downswing, whereas the investors trading in the next five cryptocurrencies exhibited herding behaviour during this market condition. The constant term of all the cryptocurrencies which represent the normal market condition were found to be statistically significant and negative for all cryptocurrencies. It can be surmised that during normal market conditions herding activity were present. However, the coefficient dl was found to be statistically insignificant and herding behaviour was not present among investors during market upswing. The residuals were examined for the presence of autocorrelation. The Ljung-Box Q-statistics, LB (36) statistic tests the null hypothesis that autocorrelations up to lag 36 equals zero implying that the residuals and squared residuals are random and independent up to 36 lags. The values of Q (36) of the Ljung-Box Q-statistics for the standardized residuals for the OLS models of the cryptocurrencies Ethereum, Ripple, Bitcoin Cash, Litecoin, Monera, Cardano, IOTA and DASH were found to be insignificant and hence the residuals were uncorrelated. The values of Q (36) and Q 2 (36) for the models Bitcoin and EOS for the standardized and squared residuals were found to be insignificant and hence the residuals were not serially correlated. The ARCH LM test is a Lagrange multiplier test to assess the significance of ARCH effects or whether the residuals are exhibiting heteroskedasticity. The ARCH LM statistic was found to be insignificant at 5 % level, confirming the removal of heteroskedasticity in the residuals. The ARCH and GARCH terms in variance equation of the models of Bitcoin and EOS were positive and significant at 5% level, suggesting persistence of volatility for longer period when a shock had occurred. The presence of herding behaviour during Covid 19 period was examined using Dummy variable OLS regression model and the results are shown in Table 2. The residual for the Bitcoin Cash CSSD and Dash CSSD regressions was found to be autocorrelated. AR(1) and MA(1) terms was added to the model of Bitcoin Cash CSSD and AR(1), AR(2), MA(1)and MA(2) terms were added in the model of Dash CSSD. The residuals for Bitcoin cash CSSD and EOS CSSD and IOTA CSSD had ARCH effects and hence GARCH (1,1) model was used. It can be seen from Table 2, that the coefficient of dl (downswing market) were positive and significant for Bitcoin (1.3349), Ethereum (1.2920), Ripple (1.7878), Bitcoin cash (1.4481), EOS (1.6419). Hence herding behavior was not evident among the investors during market upturn. The coefficient of du (upswing market) were found to be negative and significant for the cryptocurrencies Litecoin (-0.2449), Monera (-0.3705), Cardano (-0.6634), IOTA (-0.3648) and Dash (-0.4589) and the presence of herding activity was found in these cryptocurrencies. The constant term representing the herding behaviour during normal market conditions for all cryptocurrencies were found to be statistically significant and negative and the presence of herding among investors was found. Similar to the pre-Covid 19 period, the coefficients of du were positive and insignificant for Bitcoin (0.4383), Ethereum (0.4110), Ripple (0.4499), Bitcoin cash (0.0347) absence of herding was observed. However, it can be seen that the coefficients of du for the cryptocurrencies Litecoin (-0.0870), Cardano (-0.1958) and Dash (-0.3977) were negative and significant indicating that the herding behavior existed when there was upswing in the market and has negative impact on cryptocurrencies. The cryptocurrencies Monera (-0.0927) and IOTA (-0.0493) coefficients were negative and insignificant and EOS (0.7405) coefficient was positive and significant which implied absence of herd behavior. The residual statistics were verified for the presence of autocorrelation and heteroskedasticity. The insignificant values of LB Q (36) and LB Q 2 (36) showed the absence of autocorrelations in the residuals of the models. The statistic values of the ARCH effect were found to be insignificant and the absence of ARCH effects were statistically confirmed. The previous studies by Gumus et al. [START_REF] Gümüş | Herding Behaviour in Cryptocurrency Market: CSSD and CSAD Analysis[END_REF] found absence of herding behaviour for the cryptocurrencies Bitcoin, Litecoin, Stellar, Monero, Dogecoin and Dash using CCI 30 Index as market proxy whereas Silva et al. [START_REF] Da Gama Silva | Herding behaviour and contagion in the cryptocurrency market[END_REF] detected the presence of herding in 50 cryptocurrencies especially in the market stress conditions. The only study during Covid -19 by Mnif et al. [START_REF] Mnif | How the cryptocurrency market has performed during COVID 19? A multifractal analysis[END_REF] showed that herding decreased during Covid 19. The findings of this study differed by the results of Mnif et al. [START_REF] Mnif | How the cryptocurrency market has performed during COVID 19? A multifractal analysis[END_REF] It can be inferred from the above findings that the regular pattern of herding existed in the top five cryptocurrencies and Monera and IOTA cryptocurrencies. The highly technological features of the cryptocurrencies, mixed views on regulatory framework may be presumed as reasons to cause investors to herd in normal market conditions. The time series graphs of the cryptocurrencies showed a gradual fall in prices during the six months of pre Covid 19 period which may be due to the fall in global economic activities. Hence investors would have followed the trading pattern of other investors in normal market conditions. They would have tended to avoid risk and hence may not have exhibited herd behaviour during market downswing. Herding was seen in the cryptocurrencies Litecoin, Cardano and Dash during the Covid 19 pandemic period in all market conditions. It can be observed from the time series graphs that two adverse falls in prices of cryptocurrencies occurred during December 2019 and March 2020 when the outbreak of the Corona virus and the lockdown restrictions were announced. The subsequent rise in prices was also observed in the cryptocurrencies. The investors trading in Litecoin, Cardano, and Dash started to herd in all market conditions during Covid 19 period. It may be reasoned that the investors trading in these cryptocurrencies would have sought to make profits in the extreme volatility in prices. The fear of losing money may also have induced the investors to herd during the Covid 19 pandemic period. The findings would be useful to investors, academicians, and researchers. The study showed the existence and pattern of herding in the cryptocurrencies during extreme market conditions. The novel features of cryptocurrencies would cause less participation in trading of cryptocurrencies and this study shows that herding behaviour is present in all cryptocurrencies in normal market conditions. Investors can make use of this finding for their trading strategies. The research in cryptocurrency is an emerging area in the field of Technology and Finance and the study would be beneficial to academicians in both these areas and gain insights on the functioning of the cryptocurrency market. The findings of this study provide evidence on the presence of an important behavioural bias of investors, herding in the cryptocurrency market particularly during the pandemic crisis situation. There are limited studies on the behaviour of financial markets in pandemic situations and this study would be beneficial to researchers. The realization of profits due to herding in the cryptocurrency market can be taken as a future scope of this study.

  have mixed indication on herding behaviour during Covid -19 pandemic. Hence this study aims to justify a potential research gap by examining the herding behaviour during the pre-Covid 19 and Covid 19 pandemic period.

Fig. 1 .

 1 Fig. 1. Time Series Graph of Close Price of select Cryptocurrencies.

  2.88 at 5% significance level during both pre-Covid 19 and Covid 19 and were stationary.

			Bitcoin Price			Ethereum Price	Ripple Price
						400
	15000.00
	10000.00				300			0.3 0.4
	5000.00				200			0.2
		0.00	1 24 47 70 93	116 139 162 185 208 231 254 277 300 323 346	100 0	1 19 37 55 73 91	109 127 145 163 181 199 217 235 253 271 289 307 325 343 361	0.1 0	1 20 39 58 77 96	115 134 153 172 191 210 229 248 267 286 305 324 343 362
		Bitcoin cash Price					EOS Price	LitecoinPrice
	300 400 500 600					3 4 6 5				100 150
	200					2				50
	0 100	1 21 41 61 81	101 121 141 161 181 201 221 241 261 281 301 321 341 361	1 0	1 18 35 52 69 86	103 120 137 154 171 188 205 222 239 256 273 290 307 324 341 358	0	1 21 41 61 81	101 121 141 161 181 201 221 241 261 281 301 321 341 361

Table 1 .

 1 Dummy Variable OLS Regression results for herding behaviour on pre-Covid 19

	Crypto-	Log Bitcoin	Log Ethereum	Log Ripple	Log Bitcoin-	Log EOS
	currencies	CSSD	CSSD	CSSD	cash CSSD	CSSD
	Variable	Coeff. Prob. Coeff. Prob. Coeff. Prob. Coeff. Prob. Coeff. Prob.
			Mean Equation
	Du	-0.113 0.841 0.493 0.115 -0.468 0.157 -0.338 0.351 -0.034 0.88
	AR (1)		0.231 0.009
			Variance Equation
	C	0.817 0.009				0.692 0.011
	ArchTerm 0.131 0.166				0.343 0.071
	GARCH	-0.17 0.678				-0.337 0.302
	Adj R-sq	0.062	0.125	0.079	0.074	0.084
	LB Q (36) 0.115	0.204	0.970	0.288	0.529
	LB Q 2 (36) 0.840				0.610
	LM Arch	0.824	0.845	0.347	0.572	0.432
	Crypto-	Log Litecoin	Log Monera	Log Cardano	Log IOTA	Log Dash
	currencies	CSSD	CSSD	CSSD	CSSD	CSSD
	C	-6.79 0.00	-6.792 0.000 -6.808 0.000 -6.805 0.000 -6.807 0.000
	Dl	-0.27 0.000 -0.522 0.000 -0.192 0.00 -0.495 0.000 -0.310 0.000
	Du	-0.03 0.503 -0.037 0.417 0.003 0.932 0.023 0.750 0.002 0.960
	Adj R-sq	0.193	0.457	0.122	0.231	0.202
	LB Q(36)	0.054	0.996	0.430	0.999	0.705
	LB Q 2 (36) 0.632	0.745	0.882	0.949	0.857
	* Bold indicates significance at 5% level

C

-6.820 0.000 -6.873 0.000 -6.783 0.000 -6.880 0.000 -6.808 0.00 Dl 1.125 0.001 1.207 0.018 1.161 0.001 1.301 0.000 1.195 0.01

  wherein it was found that similar herding behaviour was found seven cryptocurrencies Bitcoin, Ethereum, Ripple, Bitcoin cash, EOS, Monera and IOTA during pre Covid 19 and Covid 19 periods. However, the investors trading in Litecoin, Cardano, and Dash exhibited herding behaviour during market upswing, downswing, and normal conditions in the Covid 19 period. The fear and uncertainty among the investors trading in these currencies would have led them to imitate the trading behaviour of others anticipating positive returns during the market upswing. It can be presumed that these investors demonstrated excessive herding behaviour due to fear of losing money in the Covid 19 period due to the market Financial markets react to natural calamities, epidemics political elections, news announcements. The outbreak of Covid 19 pandemic and the rapid transmission of coronavirus across global economies have caused an adverse effect on the global economy and has disrupted the lives of people across the globe. While several epidemics such as the influenza, SARS, Ebola, HINI, HIV/AIDS have posed challenges in terms of lives of people and the economic activity, the impact of Covid 19 pandemic is found to be much severe. The lockdown restrictions imposed by several countries during March 2020 to contain the spread of the virus has brought the economic activity and the lives of people to a standstill which has further aggravated the severity in global economic condition. The impact was reflected in financial markets and the market crash and halt of trading activities of several global financial markets was witnessed during this time. The cryptocurrency market which gained momentum in terms of trading during recent times also experienced the impact of Covid 19 pandemic. The investors tend to herd during situations of uncertainty and extreme volatile conditions. The reasons may be attributed to investor fear or motive to gain profits in volatile conditions. In this context, this study was carried out to examine the presence of herding activity of investors during the pre-Covid 19 and Covid 19 period. The leading ten cryptocurrencies in terms of market capitalization value was chosen and CSSD approach was employed to examine the herding behaviour of investors. The findings of the study revealed that all the ten cryptocurrencies exhibited herding behaviour during normal market conditions during the entire sample period. Herding behaviour was not evident in top five cryptocurrencies Bitcoin, Ethereum, Ripple, Bitcoin cash and EOS under situations of market upswing and downswing during the pre Covid 19 and Covid 19 period. Herding was present during market downswing in the next five cryptocurrencies Litecoin, Monera, Cardano, IOTA, and DASH during the pre Covid 19 and Covid 19 period.

	LM Arch	0.076	0.148	0.860	0.401	0.854
		* Bold indicates significance at 5% level
	5	Discussions and Conclusion

crashes, economic downturn, and uncertainty across global economies. Studies by Conrad et al. [START_REF] Conrad | Long-and cryptocurrency volatility components: A GARCH-MIDAS analysis[END_REF], Silvia et al. [START_REF] Da Gama Silva | Herding behaviour and contagion in the cryptocurrency market[END_REF], Vidal-Thomas et al. [START_REF] Vidal-Tomás | Herding in the cryptocurrency market: CSSD and CSAD approaches[END_REF], Junior et al. [START_REF] Raimundo Júnior | Market Stress and Herding: A New Approach to the Cryptocurrency Market[END_REF], Jalal et al. [START_REF] Jalal | Herding Behaviour and Cryptocurrency: Market Asymmetries, Inter -Dependency and Intra-Dependency[END_REF] also confirmed the presence of herding behaviour during extreme market conditions.


E 1 Pradeep
email: pradeepe@srmist.edu.in

A Study on Attributes of Websites with Specific Reference to Online Purchase Intentions of Baby Products in Chennai

Keywords: Baby products, Consumer behaviour, Online purchase, Trust and privacy, Website design

Baby products, our area of interest, are very sensitive products which are used by tender infants and little kids. Therefore, parents exercise great care while purchasing them and only go for the best, which explains our need to study the online purchase intentions of baby products. Working women with financial resources of their own and coupled with availability of huge information on the internet have empowered them to make informed decisions while purchasing for their babies and kids. Most of the baby products are available online and customers now prefer to buy the products through online shopping websites. But product sales through online particularly relies on the trust of the company and the company website. This article tries to analyze the appearance of the website, features and other related factors of the online shopping portal and their influences on making purchase decisions for baby products among the customers in Chennai. In this regard, data were collected from 174 respondents, through a survey method, by using structured questionnaire and the judgemental sampling method was adopted. Collected data were analysed and results revealed that trust and usability factors were significant. The results also revealed the most important factors for designing a website for achieving more customer satisfaction.

Introduction

Indian Economy is growing at a fast pace due to the surge in the working age population. Household incomes and therefore, spending capacity has greatly increased. Likewise, the disposable incomes of young parents have been on the steady rise and most of them live in urban areas as nuclear families. Both the parents are working and in particular, the working women are looking forward to use safer and better products for their babies. They are also willing to pay more for the premium quality. India has a huge birth rate too every year compared to other countries. Hence there is a huge potential for the baby care products' segment in India. Also CAGR has pointed out that the projected growth rate of baby care market is over 17% till 2020 [START_REF] Rishu | Entrepreneur India, Web online portal[END_REF].

There is a huge array of products available for babies like diapers, wet wipes, powders, baby baths liquids, shampoos, dresses, baby food, baby furniture, toys, books and many more. Due to the surplus of information available in the internet they are able to compare and contrast between various brands and choose the right product for their babies.

Due to the busy lives of people and hectic work schedules, there is a huge surge in e-commerce purchases. When people are already familiar with the product and there is no need to go and physically examine them before buying, people prefer to purchase them online, so that it reaches their place without the hassles of commuting to the shop. Hence online shopping now plays a pivotal role in our lives. There are a lot of online shopping portals now available. For the e-commerce website to be a huge success and reap profits, it is essential to keep the website attractive and safe for customers. Customers should feel comfortable and find it easy to purchase through online. Websites have also started to use Artificial Intelligence to effectively tap more customers.

Therefore, there is a big market for those who do online purchases, especially for baby products and hence this explains our need to study the online purchase intentions of baby products. In this study, Chennai region is alone considered as it is the hub of both buzzing technological and economic activity. Later on, this study can be expanded to other regions also.

Review of literature

Online purchase is greatly influenced by perceived usefulness [START_REF] Chiu | Gender differs: assessing amodel of online purchase intentions in e-retail service[END_REF]. Findings show that the usability, entertainment, and complementarity of economy hotel websites positively affected eTrust which in turn positively influenced online booking intentions [START_REF] Li | An empirical study on the influence of economy hotel website quality on online booking intentions[END_REF]. Proper design strategies pertaining to every stage of the purchase decisionmaking process have to be planned by the online merchants who have adopted social commerce, so that the online portals meet the needs and desires of customers [START_REF] Huang | The effects of social commerce design on consumer purchase decision-making: An empirical study[END_REF].

Consumers' purchase intentions are greatly influenced by the website quality and it increases product appeal. Purchase intentions are increased when the trust in ecommerce sites increases and also there exists a positive relationship between quality of the website and purchase intentions [START_REF] Liu | The art of appeal in electronic commerce: Understanding the impact of product and website quality on online purchases[END_REF]. Major factors which influence the development of e-loyalty are hedonism, privacy, e-satisfaction and customer service [START_REF] Ahmad | Exploring the role of website quality and hedonism in the formation of e-satisfaction and e-loyalty: Evidence from internet users in India[END_REF]. Website quality, design and information should be given more opportunity to improve website quality [START_REF] Sam | Website quality and consumer online purchase intention of air ticket[END_REF] One of the finding says that the online purchase intentions are related to the ease of browsing and using. The added information should be true and good interactions support online purchase intentions [START_REF] Leonidio | Evaluation of perceived quality of the website of an online bookstore: an empirical application of the Barnes and Vidgen Model[END_REF]. Another study says that, Customers online purchase intentions are positively associated with website quality [START_REF] Hasanov | The impact of website quality on online purchase intention of organic food in Malaysia: A web-Qual model approach[END_REF].

Cho.V, [START_REF] Cho | A study of the roles of trusts and risks in information-oriented online legal services using an integrated model[END_REF] said that there is a hierarchical relationship among the constructs. The most important construct was the perceived usefulness for online services. Perceived risk and trust were less important mostly on online legal services. Findings of another study show that website quality has a direct and positive impact on customer satisfaction which in turn has an impact on purchase intentions. The influence of website quality on purchase intentions significantly mediates the effect [START_REF] Bai | The impact of website quality on customer satisfaction and purchase intentions: Evidence from Chinese online visitors[END_REF]. Reputation had a positive effect on consumers' emotion and negative effect on perceived risk [START_REF] Kim | Effects of reputation and website quality on online consumers' emotion, perceived risk and purchase intention: Based on the stimulus-organism-response model[END_REF].

It is a product category where parents are initially unable to judge brand performance as consumption is undertaken by babies or toddlers. Manufacturer-branded baby-care products were regarded by respondents to be of higher quality and lower perceived risk than retail-branded baby-care products [START_REF] Broadbridge | Retail-brand baby-products: What do consumers think?[END_REF]. Understanding the purchase pattern of parents would help firms in formulating strategies to cater to the needs of the consumer and thereby increase their market share [START_REF] Kalaiselvi | Women's perception towards baby food with special reference to Erode town[END_REF].

Normative-beliefs have been found as the most significant to influence students' intention for shopping on Internet [START_REF] Yulihasri | Factors that Influence Customers' Buying Intention on Shopping Online[END_REF]. To develop a successful e-business online store, it is necessary to have a deep understanding on the trust developed and how it affects online purchase intentions [START_REF] Ganguly | Website characteristics, Trust and purchase intention in online stores:-An Empirical study in the Indian context[END_REF].

Customers' perception about the website is greatly affected by trust and perceived risk which in turn their purchase intentions [START_REF] Chang | How a branded website creates customer purchase intentions[END_REF]. There is a positive correlation between the overall perceived quality and the purchase intention when considering the company's website [START_REF] Kourtesopoulou | The Impact of Online Travel Agencies Web Service Quality on Customer and Purchase Intentions[END_REF].

Research Problem

Research focus

While doing literature study, the researcher came across scarce literature support for baby products based studies, whereas studies related to online purchase intention and behaviour were found to have quite reasonable numbers. Baby product segment is very sensitive since new born babies are very sensitive towards accessing their products such as clothes, cosmetics, toys etc. Since the current generation is more focussed towards the single child concept, every parent is very keen on choosing their baby products. Although they have more experience on online purchase, they struggle while doing the same for purchasing baby products because they may not be aware of choosing criteria due to the single child concept and sensitive nature of new born babies. Hence the researcher would like to support and enlighten the new parent's online purchase behaviour towards baby products buying with a comfortable manner. This study also supports E-Retailers in order to design their websites in a more user friendly manner. Hence this study was conducted with the following objective.

Objective of the study

The objective of this study is to analyse the various attributes of E-retail websites by focusing the buying behaviour of baby products.

Research Methodology

This research was designed based on a descriptive method. Scope of this study was confined within Chennai city. Scopes of respondents were limited to the people who are purchasing baby products online. It was not mandatory that the respondents should have kids; instead they could buy baby products for others such as other family members, relatives and friends for gifting purposes. Structured questionnaire was prepared which includes basic demography details, buying behaviour and website attributes related questions. Data were collected from 200 respondents, of which 26 were rejected due to poor and incomplete responses. Finally 174 responses were considered for data analysis. Data was collected through a survey method by using nonprobability cum judgemental sampling methods, because the data collection person has to make sure, whether they have purchased any baby products online earlier, before giving the questionnaire to a respondent. Collected data were entered into the SPSS software package and analysed by using various statistical tools such as frequency analysis, factor analysis, reliability analysis and regression analysis, and results were given in the following section.

Data Analysis and Results

Frequency Analysis

Frequency analysis was carried out for demography details just to get basic understanding about the respondents. Data were collected from 54% of male respondents and 46% of female respondents. Among them 53% were between 21 to 30 years of age and 47% were above 30 years. This study includes 28% of unmarried respondents and 72% were married. Education qualification of the respondents was 10% Diploma holders, 60% Undergraduates, 28% Post graduates and 2% with Ph. D. Among the total respondents 13% were self-employed, 17% were homemakers, 63% private employees, 4% government employees and 3% doing various other professions. Around 69% of the respondents were below an income level of Rs.30,000 per month and remaining 31% above Rs.30,000 per month. This study includes 37% of respondents from North Chennai zone, 44% from Central Chennai and 19% from South Chennai zone. Among the total respondents 36% had children below 4 years and 64% didn't have kids below 4 years. Around 41% of respondents had one child less than 4 years, 4% had two children less than 4 years and 55% had children greater than 4 years. Among the total respondents around 54% of the people purchase less frequently, 20% purchase always and 26% purchase only if someone recommends. Among all respondents around 43% of the people purchased for their own kids, 30% purchase for their family members and other 27% for gifting purposes. Around 37% of respondents were using mobile phones, 48% laptops, 9% desktops and 6% tablets for purchasing baby products through online.

Factor Analysis

Factor analysis was used to minimize the multiple variables into few factors, so that variables could be categorized under specific attributes. Factor analysis was presented by using three different segments such as KMO and Bartlett's test, total variance explained and rotated component matrix. KMO test was used to justify the sampling adequacy. Table 1 shows that, sampling adequacy value of KMO test was 0.852.

It denotes that sampling adequacy for this study was 85% and it was considered as adequate. Bartlett's test of factor analysis shows that significant value of all the factors which consider for this study and resulted as significant, since the significant value was 0.000 (p ≤ 0.05). The total variance explained through factor analysis by all variables was 72% (Table 2) with four different factors (components). All these four factors were extracted by using principle component analysis method. Loadings of each variable were determined and rotated by using Varimax rotation method. Rotation converged in 9 iterations. All the variables categorized under these four different factors based on the loading value of 0.5 and above.

Table 3 shows the different variables in specific factors. First factor includes six variables such as not vulnerable to hacking, information security features, satisfied on addressing issues, proper security wall, handling financial transactions and carries out the functionalities seamlessly. This factor named as Trust. Second factor includes four other variables such as options provided, sorting filters, comparison of features and navigating the sites. This factor named as Web Design. Third factor includes four variables such as easy to use and buy, easy interaction, easy to understand and operating speed. This factor named as Usability. Finally fourth factor includes three variables such as amount of information, accuracy of the information and ease of applying the promotional offers. This factor named as Information quality. Reliability of these four factors were also analysed by using Cronbach's alpha value. First factors show that 93% (α = 0.927) of reliability value. Second, third and Out of four factors, trust and information quality factors were turned to be significant factors since their significant values were 0.022 and 0.022 respectively. Influence of each factor decided based on Unstandardized Coefficients values. For example, increase of one unit in trust factor would influence 0.135 units of customer satisfaction. Similarly increase of one unit in information quality factor would increase 0.083 units of customer satisfaction. Whereas, importance of these factors were decided based on standardized coefficients. In this study Information quality factor (0.393) was identified as very important factor in customer satisfaction and followed by trust factor (0.331). Hence this study strongly recommends online retailers to concentrate more on quality of information and trust factors such as payment gateway, product quality, and safety of kids etc., while designing their websites in order to increase their customer satisfaction.

These findings were validated through literature support. Review of the literature section of this article is substantiating these findings, because most of the literatures were documented by supporting trust, quality of website and its information to be considered as most important factors while designing E-retail websites. Though these variables were given for general E-retail purposes, this study revealed that those factors not only important to general online purchase, but also significant for online buying of baby products too.

Conclusion

Baby care segment is in great demand due to a steadily growing economy and increase in disposable incomes. Also online shopping is more and more gaining popularity due to the ease of access in buying products. Most of the baby care products are also bought online owing to a huge segment of working mothers. Hence there is always a huge potential for purchasing baby care products, online. This study finds out that quality of information and trust based factors were identified as very important factors while designing baby products E-Retail websites in order to achieve more customer satisfaction. Moreover, the parents of first baby also could validate and choose the best websites that sells baby products online through these factors by considering respective variables mentioned in this study.

Table 1 . KMO and Bartlett's Test

 1

	KMO and Bartlett's Test Parameters	Values
	Kaiser-Meyer-Olkin Measure of Sampling Adequacy.	0.852
	Bartlett's Test of Sphericity	Approx. Chi-Square	619.876
		Df	136
		Sig.	0.000

Table 2 . Total Variance Explained

 2

	Co mp	Initial Eigenvalues		Rotation Sums of Squared Loadings
	one			Cumulative		% of Vari-	Cumulative
	nt	Total	% of Variance	%	Total	ance	%
	1	8.543	50.252	50.252	4.102	24.128	24.128
	2	1.407	8.279	58.531	3.237	19.040	43.168
	3	1.292	7.601	66.132	2.802	16.482	59.650
	4	1.040	6.118	72.250	2.142	12.601	72.250
	5	.755	4.442	76.693

Table 3 . Rotated Component Matrix a

 3

			Factors (Components)
	Variables		Trust Factor	Web De-sign Fac-tor	Usability factor	Information Quality factor
	Easy to use and buy				.713
	Easy interaction					.637
	Easy to understand					.829
	Satisfied on operating speed				.831
	Options provided			.732
	Sorting filters			.596
	Comparison features		.734
	Navigating the sites		.749
	Amount of information				.715
	Accuracy of the information				.621
	Ease of applying the promo-tional offers				.515
	Not vulnerable to hacking	.651
	Information security features	.810
	Satisfied on addressing issues	.826
	Proper security wall	.789
	Handling financial transac-tions	.658
	Carry out the functionalities seamlessly	.662
	Reliability (Chronbach's Alpha value)	.927	.779		0.738	0.801
	Extraction	Method:	Principal	Component	Analysis.
	Rotation Method: Varimax with Kaiser Normalization.
	a. Rotation converged in 9 iterations.

fourth factors shows the reliability value of 80% (α = 0.779), 74% (α = 0.738) and 80% (α = 0.801) respectively and these values were highly accepted.

Regression Analysis

Regression analysis was conducted by using overall satisfaction on website service as dependent variable and all seventeen website based factors considered as independent variables. Regression analysis revealed following findings. Model summary (Table 4) in regression analysis shows that R 2 value of 0.645. This justified that 64.5% of variance explained by the variables which taken in this study. ANOVA analysis (Table 5) in the regression analysis justified that all the variables which taken for regression analysis was significant, since significant value was 0.000 (p ≤0.05)