A Study on Calendar Anomalies in the Cryptocurrency Market

Keywords: Cryptocurrency, Efficient Market Hypothesis, Calendar anomalies. 1

Cryptocurrencies are sub-classes of digital currencies. Trading of these currencies have gained momentum during the past few years and have become new investment avenues for investors. An understanding on the market anomalies, which are patterns in asset prices would help the investors to adopt suitable strategies while trading in this asset class. This study aims to examine the presence of three calendar anomalies, day of the week, turn of the month, and year end effect in the cryptocurrencies. The top five cryptocurrencies which constitute a major share of the market capitalization value are selected for the study and the period of study is from July 23, 2017 to July 9, 2020. Dummy Variable Regression using GARCH (1,1) model was employed on the log value of returns of the cryptocurrencies. The study provides evidence on the existence of anomalies during Thursdays, the months March and April, and at the turn of the year.

INTRODUCTION

Cryptocurrencies are subsets of the digital currencies [START_REF] Lee | Handbook of Digital Currency[END_REF] which gained prominence as an important type of digital currency during recent years. It has also become a new investment option for investors and trading in these cryptocurrencies have become colossal. There are more than 3500 types of cryptocurrencies being traded with an overall market capitalization of approximately $350 billion as on August 5, 2020 (Source: Coinmarketcap.com). Among the cryptocurrencies, Bitcoin began operational in the year 2009 as the first decentralised cryptocurrency. It remains as the market leader in terms of trading with a market capitalization of $208 billion as of August 5, 2020 constituting 60% of the total market share. Cryptocurrencies introduced subsequently are primarily clones of Bitcoin and other major cryptocurrencies and have novel features and certain fundamental differences. These cryptocurrencies have forayed into the market share of Bitcoin which has decreased from 86% (March 2015) to 60% (August 2020). While regulatory frameworks of cryptocurrencies are debated by policy makers across several countries, cryptocurrencies have also garnered interest of researchers. Several dimensions of the cryptocurrencies were studied by researchers, the asset class of cryptocurrencies [START_REF] Corbet | Cryptocurrencies as a financial asset: A systematic analysis[END_REF], volatility of cryptocurrencies [START_REF] Conrad | Long-and short-term cryptocurrency volatility components: A GARCH-MIDAS analysis[END_REF], dynamic linkages of cryptocurrencies [START_REF] Corbet | Exploring the dynamic relationships between cryptocurrencies and other financial assets[END_REF], investment options [START_REF] Shahzad | Is Bitcoin a better safe haven investment than gold and commodities?[END_REF] and market anomalies [START_REF] Caporale | The day of the week effect in the cryptocurrency market[END_REF]. The Efficient Market Hypothesis (EMH) laid on the foundations of the financial theories pertaining to market efficiency states that the asset prices reflect all available information. However, in real time situations the functioning of market deviates from the rules of EMH and these deviations are called anomalies. Anomalies may occur once and disappear or could occur repeatedly. Furthermore, these anomalies are categorized as fundamental anomalies, technical anomalies, and calendar anomalies. A calendar anomaly is a type of market anomaly that considers various stock markets behavior or economic effect related to the calendar, such as the day of the week, turn of the month and so on. Information on the calendar anomalies would be beneficial to investors and they can study these patterns and deploy suitable strategies to make profits. This study aims to investigate the presence of three calendar anomalies in the cryptocurrencies, the day of the week, turn of the month, and year end anomalies during the period July 23, 2017 to July 9, 2020.

LITERATURE REVIEW

A market anomaly refers to the difference in the performance of assets from its expected or assumed price path as defined by the EMH. Market anomalies and EMH are applicable to both stock and cryptocurrency market. Several studies have examined the presence of market anomalies in stock markets [7 -11].

Studies related to calendar anomalies in the equity markets have revealed several patterns on the returns of indices and stock price movements at specific time frame owing to various reasons. Chandra [START_REF] Chandra | Stock market anomalies: A survey of calendar Effect in BSE-SENSEX[END_REF] studied the calendar effect in the Indian Bombay Stock Exchange (BSE)-Sensex on basis of the turn of the month effect and timing of the month effect. Daily logarithmic market returns for ten years were analysed using the Dummy Variable Regression model. The analysis revealed that turn of the month effect as well as time of the month effect were present in the BSE-SENSEX. Market inefficiency was also established in this study. Deyshappriya [8] examined the Colombo Stock Exchange (CSE) for the presence of day of the week effect and monthly effect market anomalies from January 1, 2004 to June 28, 2013 using daily and monthly return data and grouping in two sub-sample periods comprising of pre -war period and post-war period. Ordinary Least Square (OLS) and Generalized Auto Regressive Conditional Heteroskedasticity GARCH (1, 1) regression models were employed for analysis. The stock market anomalies, both day of the week effect and monthly effect were found to exist during the war period. Safeer & Kevin [START_REF] Safeer | A study on market anomalies in Indian stock market[END_REF] investigated the presence of market anomalies on the five select companies of the BSE Sensex during the period January 2008 to December 2012. The anomalies included weekend effect, turn of the month effect, turn of the year effect in terms of price and volume and stock split effect. The presence of Monday effect was found to exist in the BSE indices from January 2008 to December 2012. However, the turn of the month effect was found to be insignificant. Shakila et al. [START_REF] Shakila | Semi-monthly effect in stock returns: new evidence from Bombay Stock Exchange[END_REF] studied the semi-monthly effect in the BSE Sensex using the daily stock returns of five sectoral indices namely Standards and Poor (S&P) BSE Auto Index, S&P BSE Bank Index, S&P BSE Consumer Durables Index, S&P BSE FMCG Index, and S&P BSE Health Care Index. The study found no evidence of semimonthly effect in the selected sectoral indices from April 1, 2007 to March 31, 2017. Rossi & Gunardi [START_REF] Rossi | Efficient Market Hypothesis and Stock Market Anomalies: Empirical Evidence in Four European Countries[END_REF] studied the presence of calendar anomalies, the January, and the weekdays' effects in the Stock Exchange Indices of France, Germany, Italy, and Spain. OLS and GARCH models were used to examine the Indices from 2001 to 2010 and the findings did not reveal existence of any significant calendar effect in these stock markets. The review from these studies showed mixed evidence on the presence of calendar anomalies on the stock markets.

The emergence of cryptocurrencies with novel features and the trading of these currencies which constitutes to very high market capitalization value has gained the attention of researchers to explore the presence of market anomalies in these currencies. Different facets of market anomalies studied by researchers in recent years include day of the week effect [START_REF] Caporale | The day of the week effect in the cryptocurrency market[END_REF][START_REF] Plastun | Market anomalies and data persistence: The case of the day-of-the-week effect[END_REF], month of the year effect [START_REF] Rossi | Efficient Market Hypothesis and Stock Market Anomalies: Empirical Evidence in Four European Countries[END_REF], persistence [START_REF] Caporale | Persistence in the cryptocurrency market[END_REF] and price overreactions [START_REF] Caporale | Price overreactions in the cryptocurrency market[END_REF]. Cryptocurrency market constitute of more than three thousand cryptocurrencies but only a few top currencies contribute to major part of the market capitalization and the studies on market anomalies were focused either on Bitcoin or a particular type of calendar anomaly. Kurihara & Fukushima [START_REF] Kurihara | The market efficiency of Bitcoin: A weekly anomaly perspective[END_REF] investigated the market efficiency of Bitcoin concentrating on the weekly anomaly from July 17, 2010 to December 29, 2016 using OLS regression. This sample period was divided into two halves and the results showed that the Bitcoin market was inefficient for weekly anomaly. Caporalea & Plastunb [START_REF] Caporale | The day of the week effect in the cryptocurrency market[END_REF] examined the presence of day of the week anomaly in the four cryptocurrencies Bitcoin, Ripple, Dash, and Litecoin for the period 2013 to 2017. Student's t-test, ANOVA, Kruskal-Wallis, Mann-Whitney, and OLS Dummy Variable Regression tests were used to analyse the entire sample period, and the sub-sample periods and evidence for Monday anomalies were found to exist only in Bitcoin currency. Kurihara & Fukushima [START_REF] Kurihara | The market efficiency of Bitcoin: A weekly anomaly perspective[END_REF] in their study found that the cryptocurrency market was inefficient but focused only on Bitcoin which was not necessarily representative of the entire cryptocurrency market and only the day of the week effect in the cryptocurrency market was examined. It can be observed from the literature review that only few studies have examined the calendar anomalies and only a particular type of calendar anomalies was studied in the cryptocurrency market. Hence this paper aims to fill this gap in the literature by examining the three calendar anomalies, the day of the week effect, month of the year effect, and turn of the year in the cryptocurrency market.

DATA AND METHODOLOGY

In this study the leading five cryptocurrencies Bitcoin, Ethereum, Tether, XRP, and Bitcoin cash were chosen for examination on the presence of calendar anomalies. These currencies constitute 80% of the total market capitalization value of the cryptocurrency market as of August 5, 2020 (coinmarket.com). The sample period considered for the study was three years from July 23, 2017 to July 9, 2020 based on the introduction of Bitcoin Cash on July 23, 2017. Data observations consisted of 1083 trading days the largest available data period for the chosen cryptocurrencies. Data relevant to the daily closing prices for selected cryptocurrencies were sourced from the website coinmaketcap.com. In general, the time series data are generally portrayed by typical distributions which are used to examine its properties and to decide on the suitable statistical tests for analysis. The characteristics for the selected cryptocurrencies were examined for Normality using Jarque Bera Statistics, Stationarity using Augmented Dickey Fuller (ADF), and Kwiatkowski-Phillips-Schmidt-Shin (KPSS) tests. The ADF test examines the null hypothesis as time series is not stationary. The KPSS test examines the null hypothesis as time series is stationary versus an alternative hypothesis that the series has a unit root. The unit root tests are examined without time series trend and intercept as the time series plot does not exhibit any specific trend (Figure 1). For longer sample periods the returns for the selected cryptocurrencies may not be normally distributed and regularly tend to show excess kurtosis and skewness which are basic with financial parameters [START_REF] Mcdonald | Distributional characteristics: Just a few more moments[END_REF] and hence, logarithmic returns of the time series data are used for further analysis.

In an efficient market the stock prices are expected to adjust to anomalies or new information very rapidly and the volatilities tend to return to its normal level [START_REF] Mcdonald | Distributional characteristics: Just a few more moments[END_REF]. Studies on calendar anomalies both in stock markets [7 -9, 11] and cryptocurrencies market [START_REF] Caporale | The day of the week effect in the cryptocurrency market[END_REF][START_REF] Kurihara | The market efficiency of Bitcoin: A weekly anomaly perspective[END_REF][START_REF] Plastun | Month of the year effect in the cryptocurrency market and portfolio management[END_REF] have used Dummy Variable OLS Regression. In this study the Dummy Variable Regression is structured to analyse calendar anomalies by adopting the rationale of Caporale & Plastun [START_REF] Caporale | The day of the week effect in the cryptocurrency market[END_REF] for the day of the week effect, Plastun et al. [START_REF] Plastun | Month of the year effect in the cryptocurrency market and portfolio management[END_REF] for month of the year and turn of the year effects in the cryptocurrency market. The size, sign, and statistical significance of the dummy coefficients provide information about the respective calendar anomalies. Equation (1) presents the Dummy Variable Regression model to examine the day of the week effect on cryptocurrencies where Ln (Ri) is the log of return of the daily closing prices of the select cryptocurrency computed using the formula Ln(Closing Price/Closing Price-1), Constant Sunday is the constant value which captures the presence of Sunday anomaly, D1 to Dn are the dummy variables that equals 1 for observations corresponding to a particular day of the week and 0 for the other days, β1 to βn are the mean return of the particular day of the week when the dummy variable equals to 1and εt is the error term for the period t.

Ln (Ri) = Constant Sunday + β1D1+…+βnDn+ εt (1)
The month of the year effect was examined using the Equation (2) where Ln (Ri) represents the log of return of the daily closing prices of the select cryptocurrency computed using the formula Ln(Closing Price/Closing Price-1), Constant December is the constant value that represents the anomaly pertaining to December month, D1 to Dn are the dummy variables used for all days of the months January to November respectively, β1 to βn are the mean return of the days of the particular month when the dummy variable equals to 1 and εt is the error term for the period t.

Ln (Ri) = Constant December + β1D1+…+βnDn+ εt (2)
The turn of the year effect is examined using Equation (3) where Ln (Ri) is the log of return of the daily closing prices of the select cryptocurrency computed using the formula Ln(Closing Price/Closing Price-1), Constant is the value that represents other days of the year, D1 is the dummy variable that is equal to 1 for the observations corresponding to all days of the last two weeks of December and first two weeks January, and 0 otherwise, β1 are the mean return of the days of the last two and first two weeks of December and January when the dummy variable equals to 1 and εt is the error term for the period t.

Ln (Ri) = Constant + β1D1+ εt (3)
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. The presence of ARCH effect was found in the residuals of the models for all cryptocurrencies and GARCH (1,1) regression model was used for the analysis of the anomalies. The results of Ljung-Box (L-B) portmanteau test exhibited the presence of autocorrelations in the residuals of the regression model pertaining to the cryptocurrency Tether and hence, Auto Regressive Moving Average (ARMA) model was used for further analysis.

FINDINGS AND DISCUSSIONS

The time series graphs and the descriptive statistics of the select cryptocurrencies provided preliminary evidence of the behaviour of the close price for the selected cryptocurrencies. From the visual analysis of the graph (Figure 1) it can be seen that all cryptocurrencies witnessed sharp increase in their prices during December 2017 and January 2018, however only Bitcoin and Ethereum showed increase during subsequent turn of the year time periods for the years 2019 and 2020. Based on the sample period, it can be observed that Bitcoin experienced a fall and subsequent rise in prices during the months March and April of all the years in the sample period, whereas it was not observed in other cryptocurrencies. Clustering of prices can be observed in Tether while other cryptocurrencies showed no specific trend in their price movements. It can be inferred from the values of skewness and kurtosis that the daily closing prices and log return of cryptocurrencies does not confirm to normal distribution. The log return of Bitcoin and Ethereum series showed that it was negatively skewed and the kurtosis value was much higher than 3 representing that the distribution is leptokurtic with fat tails indicating that the index posted negative returns during the sample period while other cryptocurrencies displayed positive returns. Normality of the cryptocurrencies was further statistically examined using Jarque -Bera statistics (Table 1). The statistic values for the cryptocurrencies were found to be significant at 5 % level of significance during the entire sample period implying that series does not meet the normality assumptions. In practice, a leptokurtic distribution is far more likely to characterize financial time series, and the residuals from a financial time series model. 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. Hence, non-normal data series has been transformed into a normally distributed data series by applying the natural log transformation and used for further analysis.

Fig. 1. Times series graphs of select cryptocurrencies

The stationarity of the closing prices of the crypto currencies were examined using unit root tests ADF and KPSS (Table 1). The critical values of the closing prices of the crypto currencies did not meet the criteria of the ADF and the KPSS test and were found to be non-stationary. The ADF results of the log returns of the cryptocurrencies showed that the t-statistic values were lesser than the critical values -1.9411 at 5% significance level. Results implied that the Null Hypothesis (Ho) was not accepted, and the series was stationary. The KPSS test statistic values were found to be lower than the critical value 0.463 at 5 % significance level. The Null Hypothesis (Ho) was not rejected, and the log returns of cryptocurrencies were found to be stationary. The stationarity of the log returns of cryptocurrencies were established and were used for further analysis.

Bitcoin ETH

Day of the Week Anomaly

The day of the week anomalies were studied by assigning value 1 for the first dummy variable on all Mondays of the sample period and 0 for other days in Equation 1. Similarly, value 1 was assigned for all Tuesdays and 0 for other days. Sundays were not assigned values 1 since Sunday was taken as the reference variable and was represented by the constant term. The preliminary results of the Dummy Variable OLS regression equation revealed that the residuals were heteroskedastic and hence, GARCH (1,1) models were applied to examine the day of the week anomaly for all cryptocurrencies.

Clustering effect of the closing prices of Tether was observed in the Figure 1 and the residuals of GARCH (1,1) model applied for Tether cryptocurrency also showed presence of autocorrelation. Hence, GARCH (1,1): ARMA (1,1) model was used to examine presence of day of the week anomaly in Tether cryptocurrency. The results of GARCH (1,1) models are provided in Table 2 and it can be observed that coefficients of Thursday were negative and significant for the cryptocurrencies Bitcoin (-0.008), Ethereum (-0.011), XRP (-0.010), Bitcoin Cash (0.017). The findings provided existence of Thursday anomalies for the four cryptocurrencies. Negative sign of the coefficient indicated that the cryptocurrencies had negative returns on Thursdays. In case of Tether, the day of the week anomaly was not significant for any of the weekdays. Adjusted r square values of GARCH (1,1) model improved from the OLS models suggested that the GARCH (1,1) model was a better fit model to study calendar anomalies. 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) and Q 2 (36) of the Ljung-Box Q-statistics for the standardized and squared residuals were found to be insignifi-cant and hence the residuals were uncorrelated. 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 % significance level, confirming the removal of heteroskedasticity in the residuals. The ARCH and GARCH terms in variance equation were positive and significant at 1% level, suggesting that once a shock has occurred, volatility tend to persist for longer periods. The findings differed from the studies by Caporale et al. [START_REF] Caporale | Persistence in the cryptocurrency market[END_REF] and Caporale & Plastun [START_REF] Caporale | The day of the week effect in the cryptocurrency market[END_REF] who showed that Bitcoin exhibited Monday anomalies having abnormal positive returns. The difference may be accounted for the high market volatility on the cryptocurrency market from December 2019 owing to the COVID -19 pandemic and the persistence of volatility due to shocks were statistically confirmed in the ARCH and GARCH terms of the GARCH(1,1) model. were not affected by the turn of the year effect. Bitcoin cash and Ethereum were found to exhibit Thursday anomalies and turn of the year anomalies. Hence, the traders can employ trading strategy that involves short positions on Thursdays and close them at the end of this day. They can take long positions during the turn of the year except for Thursdays.

CONCLUSION

This paper examines the day of the week, month of the year and turn of the year calendar anomalies of the cryptocurrencies Bitcoin, Ethereum, Tether, XRP and Bitcoin Cash using Dummy variable GARCH regression. The study provides evidence for presence of the calendar anomalies during the sample period July 23, 2017 through July 9, 2020 and confirms market inefficiency in the cryptocurrency market. The findings of the study show that Bitcoin, Ethereum, XRP, Bitcoin Cash made negative returns on Thursdays. It may be attributed to the fact that Thursday falling in the middle of the week. Investors may have presumed that less volatility would result in less returns during this time and would have planned trading activities during the beginning and end of the week to make profits. Bitcoin also exhibited March and April month anomaly with positive returns. The months March and April are the end and start of a fiscal year and it may be suggested that investors can plan their investments especially in Bitcoin cryptocurrency for reaping more profits. Ethereum and Bitcoin were the only two cryptocurrencies effected by the turn of the year effect with positive returns caused by the change in the year. This may be due to the positive sentiment of the New Year. Tether was the only cryptocurrency that was not affected by any of the calendar anomalies examined in this paper and may be attributed to the clustering or persistence in price movements. Descriptive statistics of Bitcoin and Ethereum showed negative returns during the entire sample period. Thursday anomalies showed negative returns for these two cryptocurrencies, whereas turn of the month showed positive returns for Bitcoin and year end anomaly showed positive returns for Ethereum. It may be presumed that the occurrences of the negative returns on Thursdays were more frequent than the turn of the month and year end and hence, was reflected in the entire sample period. The findings would be beneficial to the investors and traders who can make use of these anomalies to adopt suitable trading strategies and achieve higher returns in the digital currency arena. The research in cryptocurrency is burgeoning and this study identifies the various calendar anomalies in this market and would benefit researchers by providing insights on market inefficiencies for developing models on volatility and forecasting. The cause of these anomalies may be due to volatility spill over from other global markets, political uncertainties, and news events or tweets and it can be taken into consideration for future scope of this study.

Table 1 .

 1 Characteristics of cryptocurrencies

		Tether	XRP Bitcoin	Bitcoin ETH	Tether XRP	Bitcoin
			Cash			cash
		Daily Closing prices		Log normal values
	Mean	7597.8 314.38 1.002 0.414 561.54 0.0011 0.0001 0.000 0.000 -0.001
	Median	7459.6 225.63 1.00 0.309 354.46 0.0013 -0.0001 0.000 -0.002 -0.003

Table 2 .

 2 Results of Day of the Week anomaly of select cryptocurrencies

	4.2	Month of the Year Anomaly
	The month of the year anomaly was examined by assigning value 1 to dummy variable
	for January and 0 for other months and the process was repeated for subsequent months

in Equation 2. The month of December was used as the reference variable and was represented in the constant term. The results of GARCH (1,1) models applied for Bitcoin, XRP, Ethereum and Bitcoin cash cryptocurrencies and GARCH (1,1) ARMA model for Tether are provided in Table 3. As observed from the Table 3, Bitcoin was the only cryptocurrency that had positive significant values during the months March (0.016) and April (0.011) and therefore provided evidence for market anomalies during March and April. The positive value of the coefficient indicated that positive returns were earned in Bitcoin currencies and also confirmed the month anomaly during March and April. Plastun et al. [START_REF] Plastun | Month of the year effect in the cryptocurrency market and portfolio management[END_REF] has found that Bitcoin had the lowest returns during the months of July and August. Other cryptocurrencies used in this study did not show any evidence for month anomalies. It can be surmised that Bitcoin which had higher risk return characteristics (Table 1) exhibited negative Thursday and positive March-April anomalies. Hence, market inefficiency for this cryptocurrency was evident. The traders can employ suitable trading strategies to generate abnormal profits in this asset class. Residual tests to examine the presence of ARCH effect was carried out and the insignificant values indicated the removal of ARCH effects. The statistics of Q(36) and Q 2 (36) of the Ljung-Box Q-statistics also confirmed the absence of autocorrelation in the residuals for the models of Bitcoin, XRP, Ethereum, and Bitcoin cash. The significant values of AR (1), MA (1) terms of Tether model GARCH (1,1): ARMA (1,1) confirmed the absence of autocorrelations in the residuals.

Bitcoin

Turn of the Year Anomaly

The turn of the year anomaly on cryptocurrencies was analyzed using value 1 to dummy variables for the last two and first two weeks of December and January and 0 for other weeks of the year in Equation 3. Results of GARCH (1,1) models are provided in Table 4.

Bitcoin ETH Tether XRP Bitcoin Cash

Mean Equation


Mr M Prabaharan
email: prabaharmp@gmail.com

Dr M Selvalakshmi
email: selvalakshmi@tsm.ac.in

Customers Interest in Buying an Electric Car: An Analysis of The Indian Market

Keywords: Automobile Industry, Electric car, Technology acceptance model, Diffusion of innovation, customers interest

In the automobile industry, an electric car is a vehicle impelled by the electric motors utilizing energy stored in refillable batteries, as a replacement of diesel or petrol engine. The electric car has predicted to rule the Indian market in the next few years. The study aims to know the factors which make interest in buying the electric car. Integrating the Technological acceptance and diffusion of innovation model the present research proposed extended technology acceptance model. The data were collected from 600 non-commercial car owners to test the proposed model. The findings demonstrate the perceived usefulness, perceived ease of us, and attitude are positively influence the customers' interest to buy the electric car. The relative is one of the innovation factors which have a positive effect on interest to buy the electric car. The result also shows that majority of customers are interested to buy Indian brand cars which will have a low-price range.

Introduction

An electric car is a vehicle impelled by electric motors utilizing energy stored in refillable batteries, as a replacement of diesel or petrol engine which is of three kinds such as Electric powered solar vehicle, Hybrid cars (both internal ignition and batteries) and BEV (Battery electric vehicle). Special measures have been taken in India like cutting GST on EV (Electric Vehicles) to 5% against 28% for diesel and petrol engines, also has exempted 1.5 lakh INR on loan to buy EV but only 1500 cars were sold during April -December, 2019. [1]. According to economic times survey, about 90% of Indian car customers were willing to purchase an electric car if it has good infrastructure (high cost of purchase, maintenance, charging stations) proper maintenance and support system. At present, EV market dispersion is only 1% of total sales in India [2]. But it aims to reach 30% EV dispersion by 2030. In the forthcoming five years, EV is likely to replace conservative internal ignition engines [START_REF]World economic forum[END_REF]. The passenger car customers are expecting safety, reliability, performance and personalization of the electric cars over combustion cars. The performance of electric vehicles is higher compared to the conventional vehicles between 100 km and 150 km. Customers welcoming the electric cars due to lower cost with quick charging points [4]. Generally, Electric car is a major innovation to diminish energy consumption and zero-emission in the automobile segment. Initially, the electric cars will be offered at a lower price to encourage the purchase of the customer. Electric cars are environmentally friendly and the operating cost is also less compared to the combustion engine. In the subsequent few years, the leading international car brands will launch the electric cars in both cost-effective and premium price range for their customers in India. Thus, it's vital to know the customers' interest on purchasing electric cars after propelling in India.

In India 1309 (Mahindra e-Verito -523, Tata Tigor EV -491, Hyundai Kona EV -280, Mahindra e20) electric cars were acquired from April to November 2019. Currently, three electric car models are available in India, they are Tata Tigor, Mahindra e-Verito and Hyundai Kona EV (Mainak Das, ETAuto,2019). The Indian government aimed to make India the fourth largest for EVs by 2040, but it is very hard for customers to switch over from combustion engines to zero-emission cars. Now it's very significant to know the factors which make customers interest to buy electric cars in future.

The study objectives are 1) to deliver a theoretically acceptable research model that integrates TAM and DOI to know the factors which make interest in buying the electric car in the Indian market. 2) To identify the brand and type of electric cars which buyer interest to acquire.

Theoretical Model and Hypothesis Development

The electric car is an in-mint condition with zero-emission technology which is considered to be ingenious. This study is executed by integrating two well-known adaption theories.

Technology acceptance model (TAM)

TAM has set to foresee the acceptance of new technology. This model is grounded in the theory of reasoned action (TRA-psychological theory) by [START_REF] Fishbein | Belief, attitude, intention, and behavior: An introduction to theory and research[END_REF]. Technology Acceptance Model (TAM) is the theory which explains acquiring ability of user towards newly introduced technology. It could provide information about the relative likelihood of success of proposed systems early in their development, where such information has the greatest value [START_REF] Davis | A technology acceptance model for empirically testing new end-user information systems: Theory and results[END_REF]. Acceptance of new technology is entirely based on usage. The technology acceptance model (TAM) is a widely used and most impactful theory for describing the acceptance of information technology by an individual [START_REF] Silva | Davis' technology acceptance model (TAM)[END_REF]. Technology acceptance model established user attitude [START_REF] Lee | Adding innovation diffusion theory to the technology acceptance model: Supporting employees' intentions to use e-learning systems[END_REF] and perceived usefulness (PU) identified the role of perceived ease of use (PEOU) in realizing user acceptance in information systems [10] [11].

Perceived Usefulness:

The two exogenous constructs of the attitude of TAM were perceived usefulness (PU) and perceived ease of use [START_REF] Williams | A multidimensional view of EDI: testing the value of EDI participation to firms[END_REF]. Perceived usefulness (PU) reinforces the customer's belief that using the new technology will enhance and boost the performance [13] [14]. In our research, PU denotes to the expectations of the customer, which make them interested in buying electric cars. Perceived usefulness of the electric car is based on price range, performance, tax benefits, battery cost & warranty period. The Perceived usefulness has a positive effect on purchase intention [START_REF] Ma | The technology acceptance model: A meta-analysis of empirical findings[END_REF]. The past studies have supported the perceived usefulness have a significant positive effect on the intention to use [START_REF] Lee | Adding innovation diffusion theory to the technology acceptance model: Supporting employees' intentions to use e-learning systems[END_REF] Perceived Ease of Use: Perceived ease of use (PEOU) is defined as "the degree which a person believes that using a particular system would be free of effort" [START_REF] Van Der Heijden | Understanding online purchase intentions: contributions from technology and trust perspectives[END_REF]. PEOU shows the frame of mind of customers towards buying and their perception about battery charging stations, recharging time, distance from home to charging station and durability assurance. Thus, the research has supported the hypothesis that the perceived ease of use will have a positive effect on attitude use [START_REF] Ma | The technology acceptance model: A meta-analysis of empirical findings[END_REF]. Several researches had studied the PEOU have a positive effect on attitude [START_REF] Min | Consumer adoption of the Uber mobile application: Insights from diffusion of innovation theory and technology acceptance model[END_REF], [START_REF] Tu | Key factors influencing consumers' purchase of electric vehicles[END_REF], [START_REF] Kim | A global analysis and market strategy in the electric vehicle battery industry[END_REF], [START_REF] Lee | Adding innovation diffusion theory to the technology acceptance model: Supporting employees' intentions to use e-learning systems[END_REF]. Therefore, we formulate the following hypothesis. H3: Perceived ease of use will have a significant positive effect on attitude to use.

Attitude to use: Attitude refers to a person forming favourable or unfavourable feelings toward adopting a specific technology [START_REF] Kim | A global analysis and market strategy in the electric vehicle battery industry[END_REF]. The attitude of an individual is not the only factor that determines the use but also influenced by system performance [START_REF] Suki | Exploring the relationship between perceived usefulness, perceived ease of use, perceived enjoyment, attitude and subscribers' intention towards using 3G mobile services[END_REF]. The attitude refers that it leads to the intention to use and adoption of a particular technology. [START_REF] Wang | Determinants of user adoption of web''Automatic Teller Machines': an integrated model of' Transaction Cost Theory'and'Innovation Diffusion Theory[END_REF]. Attitude to use and intention to accept are the endogenous factors [START_REF] Fishbein | Belief, attitude, intention, and behavior: An introduction to theory and research[END_REF]. Prior studies had shown the presence effect of attitude on the intention to use of new technology. Several studies supported the hypothesis that the attitude will have a positive effect on the intention to use [START_REF] Min | Consumer adoption of the Uber mobile application: Insights from diffusion of innovation theory and technology acceptance model[END_REF], [START_REF] Tu | Key factors influencing consumers' purchase of electric vehicles[END_REF], [START_REF] Weng | A TAM-based study of the attitude towards use intention of multimedia among school teachers[END_REF], [START_REF] Agag | Understanding consumer intention to participate in online travel community and effects on consumer intention to purchase travel online and WOM: An integration of innovation diffusion theory and TAM with trust[END_REF]. Hence, we formed the following hypothesis H4: Attitude has a direct and positive influence on customer interest to buy electric cars.

Diffusion of innovation theory (DOI).

The innovation diffusion theory expressed to predict and interpret how users adopt an innovation [START_REF] Agarwal | A Conceptual and Operational Definition of Personal Innovativeness in the Domain of Information Technology[END_REF]. DOI Theory, proposed by E.M. Rogers in 1962 to explain to how, over time, an idea or product gains momentum and spreads through certain population or social system. The final result of the theory is that users accept a new idea, behaviour, or product [START_REF] Miller | Rogers' innovation diffusion theory[END_REF].

Relative advantage:

The Diffusion of innovation theory enables to understand the impact of innovation characteristics. The five factors that influence the adoption of innovation are Relative advantages, Complexity, Compatibility, Observability & Trialability [START_REF] Rogers | Diffusion of innovation: A cross-cultural approach[END_REF]. The relative advantage is one of the main factors of diffusion innovation theory which extend the innovation better than the idea it replaces. The greater the relative advantage of innovation the greater acceptance of new technology [START_REF] Rogers | Diffusion of Innovations[END_REF]. RA is similar to perceived usefulness in TAM. [START_REF] Moore | Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation[END_REF]. The Relative advantages (RA) has a significant positive impact on consumer attitude towards technology [START_REF] Chen | Enticing online consumers: An extended technology acceptance perspective[END_REF]. Customers focus on new technologies that are easy to use, low cost and deliver benefits in the future over existing technology [START_REF] Ahmad | Perceptions on renewable energy use in Malaysia: Mediating role of attitude[END_REF]. The past researches have suggested that the relative advantages are one of the best predictors of intention to accept new technology and it is positively related to the intention to buy [START_REF] Rezaei | Rural households' renewable energy usage intention in Iran: Extending the unified theory of acceptance and use of technology[END_REF], [36] [28]. The significant relationship between relative H2 H3 H5 H4 advantage and intention to accept have been studied by some of past research in different fields such as solar energy, renewable energy sources, online travel purchase. Hence, we hypothesize that, H5: Relative advantages have a direct and positive influence on customer interest to buy electric cars. Finally, to know the association between gender and the factors such as charging station, price range, environmentally smart and technology which customer's interest in buying the electric car [START_REF] Chiu | The market acceptance of electric motorcycles in Taiwan experience through a stated preference analysis[END_REF]. Hence, we formulate the following hypothesis.

H1: There is an association between gender and factors which make the customer interest to buy an electric car.

Fig. 1. Proposed conceptual model

Data Methodology

In this study, the research design is descriptive in nature. The purposive sampling technique had used to collect primary data from the samples. The close-ended and well-structured questions (Likert-5-point scale) had been framed to collect the data from the respondents. The questionnaire had been developed based on the Diffusion of Innovation and Technology Acceptance Model with two parts. The first part includes the degree of demographics such as gender, age, education and income slab of the respondents and the second part includes questions based on the models. The pilot was conducted among 50 non-commercial passenger car owners to check the reliability of the instrument. The data collected for this study were 660. However, 600 samples had the completed data, hence 90.9% of samples have been considered. The tools used in this research paper include percentage analysis, chi-square, correlation analysis and multiple regression with the help of IBM SPSS 20 software.

Perceived usefulness

Perceived ease of use

Attitude to use

Interest to buy

Relative advantages Relative advantage, Perceived behavioural control and Perceived ease of use.

Literature Review

Relative advantage, perceived ease of use and perceived behavioural control had a positive effect with solar energy technology adoption.

[

491

Motivation for ritualized use, Motivation for instrumental use, Perceived usefulness and Perceived ease of use.

The research exhibited that spread information system with innovative and features, the developer ought to pay consideration to users' natural inspirations as well as to their outward recognitions [START_REF] Lieven | Who will buy electric cars? An empirical study in Germany[END_REF] 2011 Electric cars Stated preference methods 1152 -Micro/city cars had showed response positively like to secondary cars. The performance was top priority to leisure, off-roaders and sports cars.

Results and discussion

Demographic profile

The table shows the demographic characteristics like gender, age, education qualification and income of the respondents The table shows that the total sample for this study was 600. As observed, the majority of respondents (58.7%) were male and female were (41.3%). A majority of the respondents (47.2%) belonged to the age limit between 30 to 35 years, the prevailing level of education is postgraduate (65.1%) followed by graduates (29.1%). A majority of respondents had the (47.2%) income slab from 10 to 15 lakhs followed by 5 to 10 (29.3%). Overall data represents that respondents are young adults, well-educated and affluent. This might be help to know the customers interest in buying an electric car. & 11.7 % owned three cars. About half of the respondents had owned a hatchback type of car. Most of the respondents (35.2%) have been using the car for two years, followed by one year (23.5 %). The majority of respondents (82.5%) are interested in buying an electric car and most of the respondents preferred to buy Maruthi brand cars followed by Hyundai.

Descriptive indicator of the car of respondents

Reliability

Reliability test is used to measure data quality and consistency in measured items. The reliability has been tested using Cronbach's Alpha. The usually accepted value of Cronbach alpha is between 0.6 and 0.7. The Cronbach alpha value between 0.8 and 0.9 is very good level [START_REF] Hulin | Can a Reliability Coefficient Be Too High[END_REF] The value obtained for in this study was 0.909 for 24 items which are greater than 0.7, so the value is acceptable. The Cronbach alpha value for each construct (perceived usefulness, perceived use of ease, relative advantage, attitude to use and interest to buy) is more than 0.6. This value shows that the data collected for this study are reliable.

Chi-square test

Test-1: Gender and factors which will make the customer interest to buy an electric car. The chi-square test was performed at a 5% level of significance and hypothesis was tested. H1 -There is an association between gender and factors which make the customer interest to buy an electric car. The Table-6a shows that the significance value is 0.000 which is less than the probability value ((p<0.05). It supports the hypothesis hence there is a significant relationship between gender and factors which make the customer interest to buy an electric car. This result clearly shows that innovativeness, environmentally smart, low taxes, safety in speed and technology are the factors which make customers interest to buy an electric car varies significantly with gender.

Correlation analysis

This analysis is the statistical method used to identify the correlations between the variables. It is also used to describe the strength of the correlation between the variables. Correlation analysis was tested to know the relationship among variables in the construct of the model. To identify the correlation between perceived ease of use, perceived usefulness, relative advantages, attitude and interest to use all variables were correlated by using this analysis. The average score of multiple items in each construct was calculated and used for more analysis [START_REF] Wang | Determinants of user adoption of web''Automatic Teller Machines': an integrated model of' Transaction Cost Theory'and'Innovation Diffusion Theory[END_REF]. The coefficient of correlation (r) value is considered weak when it from 0.10 to 0.29 and 0.30 to 0.49 its medium then 0.5 to 1 is considered strong [START_REF] Hong | Determinants of user acceptance of digital libraries: an empirical examination of individual differences and system characteristics[END_REF]. To evade multicollinearity the coefficient of correlation value should be less than 0.8 [START_REF] Kabengele | Factors associated with uncontrolled asthma in adult asthmatics in Kinshasa, Democratic Republic of Congo[END_REF]. The result shows the correlation value 0.710, which is high among all variables. So, this value is smaller than 0.80. Hence, there is no multicollinearity in this study. The dimensions in the model were positively correlated with each other. This result shows that the relative advantages such as Maximum driving speed, environmentally-friendly, maintenance expenses and acceleration of the electric car will influence the customer interest to buy the car.

6

Discussion:

Our study employed two theories -TAM and DOI to analysis influence of customers interest in buying electric cars made several theoretical contributions. However, the resemblance of two theories have been recognized and the past studies linked to know the customer interest in buying the electric cars [START_REF] Lee | Adding innovation diffusion theory to the technology acceptance model: Supporting employees' intentions to use e-learning systems[END_REF] and scales developed by combining two theories [START_REF] Moore | Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation[END_REF]. The high correlation between the perceived usefulness (r = 0. 0.672, p<0.01), perceived ease of use (r = 0.672, p<0.01), relative advantages (r=0. 0.672, p<0.01), attitude to use (r = 0.672, p<0.01), and interest to buy the electric cars by customers. These results showed that the customer interest will be high based on the perceived usefulness of the electric car. The higher perceived ease of use of the electric car will have better attitude to use and willingness to buy the electric car and these findings assenting the previous studies [START_REF] Huang | A Study of Using Technology Acceptance Model and Its Effect on Improving Road Pavement Smoothness in Taiwan[END_REF]. The result exhibits that the perceived usefulness has a direct and positive effect on the customers' interest to buy electric cars. The higher usefulness such as price range, performance, tax benefits, battery cost is considered as the most useful factors which make interest to buy the electric cars resulted in higher interest to acquire electric cars. This finding is confirming the previous studies. [START_REF] Ma | The technology acceptance model: A meta-analysis of empirical findings[END_REF], [START_REF] Lee | Adding innovation diffusion theory to the technology acceptance model: Supporting employees' intentions to use e-learning systems[END_REF], [16] [17]. The test outcome indicates that the perceived ease of use had a positive effect on the attitude to use and interest to buy. The battery charging stations, recharging time, travel distance to charge station from home and durability assurance are determining factors of the ease of use which had positive effect on interest in buying the electric car. This result makes a positive agreement with past studies [START_REF] Min | Consumer adoption of the Uber mobile application: Insights from diffusion of innovation theory and technology acceptance model[END_REF] [15] [START_REF] Tu | Key factors influencing consumers' purchase of electric vehicles[END_REF].The finding reveals that relative advantage such as environmentally-friendly, maintenance expenses, maximum driving speed & acceleration of the electric car had a positive effect on the interest in buying the electric cars, confirming pervious results that relative advantage has a positive effect on intention to use [START_REF] Bandara | Impact of Relative Advantage, Perceived Behavioural Control and Perceived Ease of Use on Intention to Adopt with Solar Energy Technology in Sri Lanka[END_REF], [START_REF] Agag | Understanding consumer intention to participate in online travel community and effects on consumer intention to purchase travel online and WOM: An integration of innovation diffusion theory and TAM with trust[END_REF].The chi-square results exposed that the factors influencing customers interest in buying the electric car varies significantly with gender. The price range, fuel elimination cost are important factors which makes gender sensitive. This result confirms the past research [START_REF] Chiu | The market acceptance of electric motorcycles in Taiwan experience through a stated preference analysis[END_REF]. The customers are interested to buy the Indian brand manufactures like Maruthi, Mahindra. The study finds that few customers have interest in electric cars because of innovativeness in it. This study helps the marketer to know the customers' interest in electric cars and understand the highest responsibility on bringing commercial value to an environmentally smart passenger car. It aids to know the role of various factors such as charging infrastructure, convenience in city use, tax benefits and maximum driving speed in building interest to buy an electric car, so that marketer can enhance the product and facilities that will result in maximum reach of electric cars in the Indian market. This study gives the empirical support to the vital role that perceived usefulness, perceived use of ease and relative advantage of electric cars which make customer interest to buy. This research is restricted to the car industry in India, so this may constraint the generalizability of other overseas countries outcome. This study mainly emphasizes on the electric car (environmentally smart), so the customers' interest in the fuelbased engine cars are not considered for this research. The survey data are collected only from non-commercial car owners. The study is based on the list of leading cars selling metropolitan cities report for the financial year 2019 [48] in India. The outcomes of the study should therefore be exercised with extreme cautiousness in non-metropolitan cities.

Conclusion:

The rapid change in technology leads to introduction of electric car in alternative to fuel engine which is considered to be linchpin in the automobile industry. It is necessary for the customers to have interest towards theory of z generation before buying electric car. Customers are aware of the factors such as charging station, price range, environmentally smart and technology and they are very much ready to accept new technology with low price range. The findings of this research given greater understandings when analyzing customers interest to buy the electric car. The perceived usefulness, perceived ease of use, relative advantage, attitude and interest to buy were the factors investigated to know the interest and willingness of customers to buy the electric cars. As a result, we proposed the integration of TAM and DOI models could offer better overall outcomes. This study helps to understand the preeminent responsibility for bringing commercial value to an environmentally smart passenger car.

Appendix

Factors Items Perceived usefulness

PU-1

The Price range is the main factor that I will consider to buy an electric car. PU-2

The Performance of the electric car is a factor that I will consider for purchase. PU-3 I accept that tax benefits (Low taxes) provided by the government will encourage the electric car's sale. PU-4 I admit that additional income tax deduction provided on the interest paid on loan taken will promote to buy an electric car. PU-5

The battery cost is a significant factor that I will consider for purchase. PU-6

The warranty period will help me to buy electric cars. Perceived ease of use PEOU-1 I am willing to own the electric car if battery charging stations assigned in a high number. PEOU -2 I consider the recharging time of the battery will play a vital role in buying an electric car. PEOU -3 The distance from home to charging station is a factor that I will consider before purchasing an electric car. PEOU -4 I will purchase if durability assurance is given by the car company. Attitude to use ATU-1 I believe that charging facility of the electric car at home will convince the buyers. ATU-2 I believe that comfort for city use is an important factor in electric car purchasing. ATU-3 I believe that charging infrastructure in high number will encourage the customer to buy the electric car. ATU-4 The Attractive design and style will stimulate me to acquire the electric car. ATU-5 I believe the short driving distance of an electric car will not affect the buying interest. Relative advantages RA-1 Maximum driving speed of the car is a significant factor that I will consider for purchase. RA-2 I am ready to pay more for the electric car since its environmentally-friendly charging car.

RA3

I am ready to spend more money to purchase an electric car to save maintenance expenses. RA-4 My expectation on acceleration is high on the electric car compared to fuel engines. Interest to buy ITB -1 I am eager to buy the Indian brand electric cars. ITB -2 I am eager to purchase an electric car as a secondary (used) car. ITB -3 I am eager to buy luxury and sports type of electric cars. ITB -4 I am willing to buy an electric car if the car company offers a micro car segment.

2

 2

Fig. 2 .

 2 Fig. 2. Test results, *: represents significant difference p<0.05:

Table 1 .

 1 Literature review

	Studies	Year	Application	Theory / Method		Samples Constructs used	Results
	[18]	2019	Uber Mobile	Diffusion of Innovation the-	300	Perceived Usefulness, Perceived Ease of	Diffusion of innovation factors had effect per-
			application	ory & Technology ac-		Use, Relative advantage, Compatibil-	ceived ease of use and on perceived useful-
				ceptance model		ity, Complexity, Observability, Social	ness of smartphone application.
							influence, Attitude & Future usage in-
							tention.
	[19]	2019	Electric Vehi-	Theory of planned behavior	336	PU, PEOU, Compatibility, Personal in-	The investigation revealed that the attractive
			cles	(TPB), Technology ac-		novativeness, External influence, Per-	battery and charging schemes are the factors
				ceptance model (TAM) &		ceived behaviour control, Attitude,	which attract consumers.
				Innovation diffusion the-		Subjective norms, Self-control, Pur-
				ory (IDT)			chase intention
	[39]	2019	Crowdsourcing	Technology	acceptance	48	Perceived Usefulness, Perceived Ease of	The research exposed that perceived usefulness
			Platform -	model (TAM),			Use & Behavioural intention	and perceived have significant positive as-
			Students					sociation on behavioral intention.
	[25]	2018	Renewable en-	Unified theory of acceptance	280	Relative advantage, Awareness, Behav-	Perceived behavioral control, relative ad-
			ergy usage	and use of technology		ioral control, Moral norms, Social	vantage, awareness, and moral norms which
				(utaut)			norms, Attitude & Intention to use.	had statistically significant positive associa-
								tions with intention
	[36]	2018	Solar Energy	Technology	acceptance	384
			Technology	model (TAM), Diffusion
				of innovation (DOI) and
				Theory of planned behav-
				ior (TPB)

Table 2 .

 2 Demographic profile

	Particulars	Levels & Percentage
	Gender	Male	Female
		58.7 %	41.3 %
	Age	21-25 years	26-30 years	31-35 years	35-40 years	Above 40
		11.7 %	11.7 %	47.2%	17.7%	11.8%
	Education	Higher secondary	Graduate	Post graduate
		5.8 %	29.1 %	65.1%
	Income slab	Below 5 lakhs	5 to10 lakhs	10 to 15 lakhs	15 to 20 lakhs	> 20 lakhs
		5.8 %	29.3 %	47.2%	17.7%	5.8%

Table 3 .

 3 Descriptive indicator of the car of respondents

Table No .-2-Descriptive indicator about the respondents' interest in the electric car.

 No

	Particulars		Levels & Percentage
	Numbers of Cars Owned	1 car	2 cars	3cars		More than three cars
		53.3 %	29.2 %	11.7 %		5.8 %
	Type of cars	Hatchback	MPV	Sedan		MPV
		47.0 %	11.8 %	29.4%		11.8 %
	How long have you been	1 year	2 years	3 years	4 years	≥ 5
	using the car	23.5 %	35.2 %	12.0 %	11.8 %	17.5 %
	Interested in buying an	Yes	No
	electric car	82.3 %	17.7 %
	Interested brand to buy	Maruthi	Tata	Renault	Mahindra	Hyundai	Honda	Ford
	electric car	29.5 %	9.8 %	14.1 %	11.2 %	17.5 %	12.0 %	6.0 %
	The table contains descriptive indicators of particulars like number, type & brand of cars. The majority (53.3 %) of respondents
	owned one car, 29.2 % owned two cars

Table 4 .

 4 Reliability test

	Factors	No. of Items	Cronbach Alpha
	Perceived usefulness	6	0.751
	Perceived use of ease	4	0.841
	Relative advantage	4	0.661
	Attitude to use	6	0.717
	Interest to buy	4	0.708

Table 5 .

 5 Frequency table -Gender vs factors which influence the customer interest to buy.

	Factors

Table 5 .

 5 Chi square values

	Value	df	Asymp. Sig. (2-
			sided)

Table 6 .

 6 Pearson correlation analysis resultsCorrelation is significant at the 0.01 level (2-tailed), * Correlation is significant at the 0.05 level(2-tailed

	Constructs	PU	PEOU	RA	ATU	ITB
	Perceived usefulness (PU)	1
	Perceived ease of use (PEOU)	0.705 **	1
	Relative advantages (RA)	0.710 **	0.612 **	1
	Attitude to use (ATU)	0.619 **	0.666 **	0.603 **	1
	Interest to buy (ITB)	0.672 **	0.661 **	0.618 **	0.625 **	1
	**

Table 9 .

 9 Coefficient a of relative advantages and interest to buy

			R 2 value -0.219 (21.9%)
	Model	Coefficient of relative advantage
		Unstandardized coefficients	Standardized coefficients	t-value	Sig.
		B	Standard Error	β
	Constant	0.866	0.160		5.417	0.000
	Relative advantage	0.336	0.037	0.349	9.128	0.000
	Dependent variable: Interest to buy

Multiple Regression Analysis

The statistical method which defines a set of predictor variables and criterion variables. It also used to understand which independent variables are related to the dependent variable. In this research, we explore the relationship among perceived usefulness, perceived use of ease, relative advantage, attitude to use and interest to buy. The proposed model was examined through the parameters such as R2, F-ratio of overall fit, residual and hypothesis testing, t-test of specific parameters. In this research, these parameters had applied to find the direct and positive effect among constructs.

Regression analysis of perceived usefulness vs interest to buy:

The hypothesis (H1) of whether the perceived usefulness will have a positive effect on interest to buy has proposed and tested results are shown in the table. The result had enumerated that the p-value was less than 0.05 (p<0.05) and t-test value (8.874) which was positive. Hence so the results support the hypothesis, so the perceived usefulness will have a positive effect on interest to buy which had the ability of elucidation of 52.6 %. (R 2 =0.526). The result indicates that the perceived usefulness of the electric car such as price range, performance, tax benefits, battery cost & warranty period, will make customer interest to buy the car. Regression analysis of perceived ease of use vs interest to buy: In the testing of proposed hypotheses (H2 & H3) in the model has extended and test results are showed in the table. The result had indicated that the p-value of PEOU and attitude was less than 0.05 (p<0.05) and t-test value was positive. Hence so the results support hypotheses and the interest to buy was significantly affected by both perceived ease of use and attitude to use which had the capability of elucidation of 55.0 %. (R 2 =0.550). The result shows that the perceived ease of use such as battery charging stations, recharging time, travel distance to charge station from home and durability assurance of the electric car will influence the attitude of the customer and make interest to buy an electric car. Both PEOU and attitude have a significant and positive influence on interest to buy. Furthermore, the attitude has a strong effect (0.437) on interest to buy then perceived ease of use (0.169).

Regression analysis of relative advantage vs interest to buy the hypothesis (H3) of whether the relative advantages have a direct and positive influence on interest to buy has proffered and test results are exposed in the table. The result had enumerated that the p-value was less than 0.05 (p<0.05) and t-test value (5.417) which was positive. Hence so the results sustenance the hypothesis, so the relative advantage will have a direct and positive influence on interest to buy and capability of exposition of 21.9 %. (R 2 =0.219).


Shirish C Srivastava
email: srivastava@hec.edu

Stéphane Cren

Re-Imagining the Use of Data Standards for Retail Products: The Case of GS1 through a Service-Dominant Logic

Keywords: Data standards, omni channel, barcode, supply chain, data as a service, service dominant logic

Though technological advancements offer a host of emergent opportunities, they also introduce fresh challenges for organizations. To overcome these challenges, organizations need to be sensitive to the evolving business needs and growing expectations of the customers. In this research-in-progress paper, through the case of GS1, we demonstrate that even for a seemingly stable business, there is a need to continuously evaluate the influence of changes in the situating technological environment, which may lead to changes in the societal demands. GS1, a non-profit organization that develops and maintains global standards for business communication is redefining itself in the face of e-commerce impacted supply chains and evolving customer needs. We posit that organizations need to espouse a service dominant logic mindset, which can help them continually assess and redefine their business models to address the evolving ecological requirements. Our ongoing work aims to identify a set of lessons for firms to tackle this perpetual challenge.

Introduction

In the current times organizations are becoming increasingly dependent on information technology (IT). From a mere support function, IT has evolved into becoming the bedrock of organization's business strategies [START_REF] Huang | Technology-driven service strategy[END_REF]. Whether it is the quest for exploring new strategic opportunities enabled by artificial intelligence and social media analytics, or enrichment of the ongoing business operations through efficient enterprise resource planning systems, the role of IT is now deeply intertwined with the core business. Given the rapid rate at which IT is advancing, and the close interdependence between the IT and business strategy, results in both opportunities and challenges for organizations -opportunities to tap into the capabilities of the technological advancements and challenges to keep pace with the rapidly evolving technological landscape that can sometimes tend to impact the underlying logic on which the organization is premised. It will be theoretically and practically interesting to examine how agile organizations are able to continuously redefine their strategies in the face of a continuously changing technological environment.

The next big obstacle after the industrial revolution was the need to develop a supply chain that could effectively and efficiently handle the distribution of the large variety of products manufactured in the numerous factories. In the non-Internet era, the retail supply chain relied on intermediaries such as warehouses, wholesales, and the retailers who were the final touch points with the end customers. However, as the number of products increased there was a growing need to standardize the "identification" of the products to ensure their error free traceability throughout the supply chain [START_REF] Teo | Fostering IT-enabled Business Innovation at YCH Group[END_REF]. This pressing supply chain need was precipitated in the US where the retail industry was searching for ways to speed-up the checkout process at the point-of-sales. After the establishment of the ad hoc committee for a uniform grocery product identification code in 1969, it was not until 1973 when the Universal Product Code (UPC) was selected as the first single standard for unique product identification. In 1974, a pack of Wrigley's chewing gum became the first ever product with a bar code to be scanned in a shop [START_REF]GS1[END_REF]. Because of the grassroots need to exchange product related data in the retail market around the globe, a non-profit organization called GS1 gradually emerged as the nodal agency that served as a platform for fulfilling the need of providing standards for product data exchange for a seamless flow of products across supply chain partners to the end customers. However, with the growth of technological capabilities for data exchange, communication and the Internet, the data related needs of the supply chain partners and customers also evolved. Instead of viewing product related barcode data merely for "identification" to ensure physical traceability, supply chain partners started looking for enhanced "description" of the products to establish informational traceability for recombing this product related information with other informational sources to create value for different customers (supply chain partners and end customers).

We examine this evolution in the needs of the supply chain customers through the lens of GS1, which is a non-profit organization that develops and maintains global standards for business communication. GS1 presents a unique study context for two reasons (1) maintaining standards, by definition appears to be a static concept with seemingly stable business logic, and (2) customers for such a business also appear to be relatively fixed. However, as aforementioned, we observe that both the standards and the customers of product supply chain standards have been evolving. GS1 has been endeavoring to meticulously address the changing demands in this evolutionary journey. Consequently, through the case of GS1 we examine the following research questions:

1. How can the technological advancements impact a seemingly stable business logic?

2. How can organizations premised on principles of stability instill agility in their organizations in response to the evolving business logic?

Grounding our study in service-dominant logic (SDL) literature and adopting a case study research approach we examine how the definition and need for data exchange evolved over time. And how because of this evolution, GS1, the organization providing the platform and standards for data exchange had to incorporate appropriate changes in its business model through innovative solutions such as CodeOnline by GS1 France. The case reiterates the importance of examining the seemingly product-centric business models through a service-centric lens. We observe that because of technological evolution, data standards, which are seemingly static attributes of products could be used for providing value added services. In addition, the study reiterates the importance of instilling agility into traditional product centric organizations that may be impacted by the enhanced informational connectivity and other technological advancements.

Theoretical Background and Study Context

Business models are premised on value creation for the key stakeholders. Traditional models of value creation are based on the goods-dominant (G-D) logic, in which a firm's output is the source of value creation. G-D logic is based on a push philosophy, viewing produced goods as the source of value for the consumer. The value produced is thus "value-in-exchange," where the product is the starting point for value creation and the product is expected to create value for the consumer when they are consumed. G-D logic thus views producers and consumers as separate entities with distinct roles in the value creation process. Producer playing the key role, conceptualized "as a series of activities performed by the firm" [4, p.146]. The consumer is only expected to benefit from the value created by the producer. Going by the described G-D logic, GS1 as the platform for the provision of data standards to the supply chain partners is supposed to provide the rules and a classification system (e.g. in the form of barcodes) that provides a language of communication across supply chain partners for seamless exchange of physical products. By definition, the application and usage of the formulated data standard rules are expected to be static over time and the primary use that could be imagined by the GS1 users is to have an error free "identification" of the physical products as they move from the producer to the end customer through the supply chain partners such as wholesalers and retailers.

Vargo and Lusch [START_REF] Vargo | Service-Dominant Logic: Continuing the Evolution[END_REF][START_REF] Vargo | Evolving to a New Dominant Logic for Marketing[END_REF] proposed an alternative view, a service-dominant (S-D) logic in which the roles of producers and consumers in the value creation process are intertwined rather than distinct. The focus is on the requirements of the service consumers (users) and the offerings of service producers (providers) that aim at creating "valuein-use." Value in the S-D logic is created jointly and reciprocally through mutual interactions, facilitating integration of resources among the producers and consumers [START_REF] Vargo | Service-Dominant Logic: Continuing the Evolution[END_REF]. This new pull-based S-D logic has a user-centric perspective in which the producer and the consumer together create value by designing processes that mutually leverage available resources [START_REF] Sheth | Relationship Marketing in Consumer Markets: Antecedents and Consequences[END_REF][START_REF] Teo | The Trident Model for Customer-Centric Enterprise System at Comfort Transportation, Singapore[END_REF][START_REF] Srivastava | Bridging the Service Divide through Digitally Enabled Service Innovations: Evidence from Indian Healthcare Service Providers[END_REF]. However, service provision entails greater use of intangible resources such as knowledge and skills for value creation, in addition to tangible re-sources such as technology or standards. Thus, in contrast to G-D logic, S-D logic emphasizes an appropriate configuration of resources-intangibles along with tangiblesto create value for consumers. In the case of GS1, the data standards are the rules that define the basis of data exchange across the supply chain partners. The implementation of these rules of standardization was left to the supply chain partners specifically the brand owners who were interested in a smooth supply chain journey for their products. However, with the growth of Internet and e-commerce there were two challenges for GS1. First, to make the data standards accessible to all the possibly interested players in the product supply chain, e.g. the small and medium enterprises (SMEs) who could now benefit from the Internet enabled supply chains. Such a shift entailed GS1 adopting a service-centric mindset to provide not only the 'data standards' but also the necessary 'toolkit' to implement these data standards. Second, because of the ease of access to information, the end customers want customized product related solutions which could be orchestrated by combining specific product related information with other contextual information e.g. to have a lunch menu based on the nutritional properties of the packaged food products. Such value-added solutions require GS1 to capture and provide the appropriate product related "information" to the ecosystem partners to enable them design customer-centric solutions.

The need to provide value-added solutions to different customer segments signifies the growing importance of operant resources in the current supply chain context. Operand resources represent tangible assets, for example, in the case of GS1 these were the rules for data standards linked with the Global Trade Item Numbers (GTIN) and bar codes for identifying products in the supply chains [START_REF]GS1[END_REF]. However, now GS1 sees a need to shift to develop operant resources, which are generally invisible and intangible and shaped out of knowledge and recombined capacities, for example, the GTIN implementation tool kit or customized product related solutions. In our study, we examine this shift and also identify the practices that GS1 adopted to overcome the challenges for developing the operant resources.

Method

This research aims at identifying the specific actions that GS1 took to overcome the challenges imposed on it because of the growing importance of e-commerce and online supply chains. Specifically, we first identify the challenges faced by GS1 and then examine the key actions taken by GS1 to overcome the identified challenges. We adopt a systematic retrospective longitudinal case study research approach to examine how the definition and need for product data exchange evolved over time and how because of this evolution, GS1 had to incorporate appropriate changes in its business model through innovative solutions such as CodeOnline for providing access to an enhanced set of supply chain partners, or initiating efforts to capture product related data which could perhaps be used by ecosystem partners for providing value added customer-centric solutions.

Consistent with the case study research approach, we will provide an overview of some of the streams of research that will contribute to our analysis, namely, service dominant logic in conjunction with literature on operand and operant resources [START_REF] Srivastava | Bridging the Service Divide through Digitally Enabled Service Innovations: Evidence from Indian Healthcare Service Providers[END_REF][START_REF] Nambisan | Information technology and product/service innovation: A brief assessment and some suggestions for future research[END_REF][START_REF] Madhavaram | The service-dominant logic and a hierarchy of operant resources: developing masterful operant resources and implications for marketing strategy[END_REF].

Our study uses a mix of primary and secondary data to analyze the research questions. We have already conducted 5 interviews and have attended several presentations given by the ecosystem partners and management team at GS1. Some of the key respondents were interviewed multiple times to clarify the facts as they emerged during the data collection process. We have audio recorded and transcribed all the interviews conducted thus far, which provided us with about 100 pages of single-spaced transcripts. We also took detailed notes during all interviews. For secondary data, we went through the organizations' activity reports, websites, published articles, internal reports, and video clips. We are using a longitudinal process view to analyze the data, aimed at understanding the actions taken in response to the specific challenges encountered [START_REF] Teo | A Framework for Stakeholder Oriented Mindfulness: Case of RFID Implementation at YCH Group, Singapore[END_REF][START_REF] Chakraborty | An Exploration into the Process Requirements Elicitation: A Grounded Approach[END_REF][START_REF] Lyytinen | Explaining Information System Change: A Punctuated Socio-Technical Change Model[END_REF]. In the coming months, we intend to interview two other categories of GS1 stakeholders (1) supply chain partners who are using GS1 services directly, and (2) final consumers of products, who need new value-added services.

Preliminary Findings and Expected Implications

Technological advancements do offer new opportunities but may also be instrumental in inducing unprecedented challenges even for traditionally product-centric organizations. GS1, a global non-profit organization, established for determining and maintaining commonly agreed data standards for product identification, faced challenges with the growth of Internet in two ways. First, the growth of Internet heralded the use of e-commerce which initially connected the big brands directly to the end-customers. However, with the passage of time marketplace platforms such as Amazon, replaced the direct sales from the brand owners. Such marketplaces are very similar to the physical supermarkets with some major differences: (1) the products sold through marketplaces do not need to be physically scanned at the checkout, which obviates the need for barcodes, (2) marketplaces could stock and sell white-labelled goods from unknown suppliers, which may not have a GS1 allocated GTIN, (3) marketplaces could stock and sell integrated/assembled products from different sub-suppliers where the individual sub-components may have different GTINs.

Second, because of the increased Internet enabled connectivity end consumers have increased access to information. The product needs for these informed consumers has become more sophisticated with the growing demand for customized products. Consumers would need more information about the products before purchasing them e.g. nutritional value related information for food products. In fact, if such product related information is readily available, entrepreneurs could work out customized solutions for specific consumers based on their tastes and preferences e.g. proposing a nutritious vegetarian meal to a diabetic consumer.

From the two aforementioned challenges, it is clear that GS1 product data standards, which were till now required primarily for an error free product identification and used mainly by big brands, needs to be reimagined in two ways (a) the GTIN standards need to made accessible to all possible users of e-commerce supply chain such as owners of SMEs and white-labelled product manufactures, and (2) GS1 needs to provide more product related information so that customized value added solutions can be proposed to different consumer segments by GS1 or its ecosystem partners.

Hence, there is a definite shift in the use of data by GS1 from a mere "identification" of products to getting detailed "information" about the products, which can be used to create value-added solutions for the consumers. GS1 is currently focused on overcoming these challenges through various initiatives such as CodeOnline and involvement of ecosystem partners to offer customizable solutions to different customer segments. In our study, we are delving deeper into the different actions taken by GS1 in this transformational journey from "data as a product" to "data as a service". Our study hopes to unearth specific lessons for traditionally product-centric organizations contemplating a shift to the emerging service-centric milieu because of the enhanced technological connectivity and information access.


Souma Kanti Paul

Dr Sadia Riaz

Dr Suchismita Das

Organizational Adoption of Artificial Intelligence in Supply Chain Risk Management

Keywords: Adoption, Artificial Intelligence, Supply Chain Risk Management

With the growing complexity of global supply chains, geopolitical events, pandemics, and just-in-time processes, organizations can benefit immensely in managing supply chain risks by adopting artificial intelligence (AI). Building upon past research in technology adoption, we study factors influencing the adoption intention of AI in SCRM across organizations in India. Based on a qualitative study, we discuss the applications and uniqueness of AI adoption in the field of supply chain risk management (SCRM) and propose a research model on the adoption, implementation, and routinization intention of AI in SCRM at an organizational level. Secondly, we discuss the implications of the study and the benefits to decision-makers and supply chain planners in devising effective strategies when adopting AI in SCRM.

Introduction

Growing incidences of events like natural disasters, pandemics, just-in-time processes, and global supply chain networks have resulted in increased vulnerability of supply chains to disruptions [START_REF] Snyder | OR/MS Models for Supply Chain Disruptions: A Review[END_REF]. A report [2] on the impact of Covid-19 on global Gross Domestic Product (GDP) cites that more than five million companies, including greater than 90% Fortune 1000 companies, had one or multiple tier-2 suppliers in the impacted region in China during the initial months of the pandemic. There has been a growing interest in the application of Artificial Intelligence (AI) in Supply Chain Risk Management (SCRM). However, there has been limited research on the adoption of AI in SCRM at an organizational level across industry verticals in India. The objective of this research is to identify factors that influence the adoption intention, implementation intention, and routinization intention of AI in SCRM at an organizational level in India.

The industries considered for this study cover consumer-packaged-goods (CPG), consumer durables, wholesale, logistics, and retail.

Literature Review

Definition of AI

The field of AI has evolved over the past few decades and accordingly, the definition and scope of AI have been continually evolving. Today, Artificial Neural Networks (ANN) and Deep Learning (DL), form the core of applications classified under AI [START_REF] Haenlein | A Brief History of Artificial Intelligence: On the Past, Present, and Future of Artificial Intelligence[END_REF].

Accordingly, for this study we consider deep learning techniques that use artificial neural networks as the definition of AI.

Definition of SCRM

Based on past research, SCRM is defined as the management supply chain risks through active coordination between all supply chain partners to identify, assess, and respond to risks ensuring disruptions are mitigated leading to business continuity and profitability [START_REF] Colicchia | Supply chain risk management: A new methodology for a systematic literature review[END_REF]. It should also be noted that SCRM is a part of Enterprise Risk Management (ERM) [START_REF] Curkovic | Supply Chain Risk Management within the Context of COSO's Enterprise Risk Management Framework[END_REF].

Application of AI in SCRM

Recent studies have argued that AI will fundamentally change the way organizations make decisions and interact with their external stakeholders [START_REF] Haenlein | A Brief History of Artificial Intelligence: On the Past, Present, and Future of Artificial Intelligence[END_REF]. Due to the rise of big data and advances in computing, it is today possible to analyze billions of real-time transactions, process a wide range of unstructured data like pictures, videos, and natural language, and generate predictive and prescriptive insights for decision making. These advances in the field of AI stand to benefit SCRM immensely and hence the growing interest in the application of AI in SCRM. Recent studies cite the use of AI to predict the probability of occurrence of risks and costs of risks [START_REF] Ojha | Bayesian network modelling for supply chain risk propagation[END_REF], reduce risk of churn in distribution management [START_REF] Necula | Deep Learning for Distribution Channels' Management[END_REF], predict damage parameters in logistics [START_REF] Gürbüz | Prediction of damage parameters of a 3PL company via data mining and neural networks[END_REF], and forecasting the level of integration of supply chain to minimize risks [START_REF] Muñoz | Application of neural networks in predicting the level of integration in supply chains[END_REF].

The Uniqueness of AI Adoption

Organizational agility, in terms of rapidly responding to external shocks like pandemics or geopolitical situations, adapting to market changes, or responding to customer demands plays a critical role in AI adoption. It is also important to note that the implementation of AI solutions, often in conjunction with other advanced digital technologies [START_REF] Brock | Demystifying AI What Digital Transformation Leaders Can Teach You[END_REF], involves an entire ecosystem of technologies right from high-end computing hardware, big data platforms, data processing tools, analytical tools, and open source technologies. Moreover, AI adoption, implementation, and routinization intentions in SCRM greatly depend on the integration and compliance of AI solutions to ERM policies and processes.

Applicability of Technology Adoption Theories to AI Adoption in SCRM

The Technology-Organisation-Environment (TOE) framework [START_REF] Tornatzky | The process of technology innovation[END_REF] identifies three independent contexts that influence the adoption, implementation, and routinization intention of technologies at an organizational level. Based on the TOE framework, past researchers have studied the adoption intention of technologies like Big Data Analytics [START_REF] Chen | How the Use of Big Data Analytics Affects Value Creation in Supply Chain Management[END_REF] and Predictive Analytics [START_REF] Banerjee | Determinants of Analytics Process Adoption in Emerging Economies: Perspectives from the Marketing Domain in India[END_REF]. But there is clearly a need to study the applicability of the TOE framework for adoption in the field of SCRM. A recent study cites that managers of global organizations have limited empirical advice on how to implement AI in the companies' operations [START_REF] Brock | Demystifying AI What Digital Transformation Leaders Can Teach You[END_REF]. It also calls for a study on the current prevalence of AI in business and the exploration of key dimensions of AI implementations. Hence, it is important to study the implementation intention and routinization intention of AI and not just the intent to adopt AI. Given the earlier cited uniqueness of AI adoption in SCRM, the authors suggest using the three contexts of the TOE framework, namely technological context, organizational context, and environmental context but include factors that are newly added and defined by the authors in the context of AI.

Research Model and Hypothesis

The authors have used keyword-based search of past technology adoption studies indexed in ProQuest, EBSCO, and Google Scholar, coupled with interviews of nine industry subject matter experts (SMEs) in India to identify and validate independent factors as part of the technological, organizational, and environmental contexts of TOE framework. The adoption intention, implementation intention, and routinization intention of AI in SCRM have been identified as dependent variables.

Technological Context

The authors argue that organizations that have large and diverse datasets available across supply chain systems [START_REF] Chen | How the Use of Big Data Analytics Affects Value Creation in Supply Chain Management[END_REF] like transactional data, pictures, videos, texts, and real-time data are in a better position to initiate AI projects. Past studies have shown relative advantage and complexity as oft used factors to study technology adoption [START_REF] Awa | Integrated technology-organization-environment (T-O-E) taxonomies for technology adoption[END_REF] and is applicable to AI as well. Also, the cost of ownership is a critical determinant [START_REF] Chen | How the Use of Big Data Analytics Affects Value Creation in Supply Chain Management[END_REF] of AI adoption in a market like India. Taking these factors into consideration, the following hypotheses have been proposed: (H1) Availability of big data is positively related to AI adoption intention, AI implementation intention, and AI routinization intention in SCRM, (H2) Relative advantage is positively related to AI adoption intention, AI implementation intention, and AI routinization intention in SCRM, (H3) Complexity is negatively related to AI adoption intention, AI implementation intention, and AI routinization intention in SCRM, and (H4) Cost of ownership is negatively related to AI adoption intention, AI implementation intention, and AI routinization intention in SCRM

Organizational Context

Apart from organizational characteristics like location and size, organizational awareness of AI's possibilities and understanding of how to best utilize the capabilities of AI in SCRM is an important determinant [START_REF] Brock | Demystifying AI What Digital Transformation Leaders Can Teach You[END_REF]. Organizational agility has been cited as a key determinant of AI success [START_REF] Brock | Demystifying AI What Digital Transformation Leaders Can Teach You[END_REF]. The availability of talent has been cited as an important indicator of the adoption of innovation [START_REF] Queiroz | Big data analytics in supply chain and logistics: An empirical approach[END_REF]. As cited by past research, one of the best predictors is leadership support by influencing the organization's intention to adopt innovative technologies [START_REF] Banerjee | Determinants of Analytics Process Adoption in Emerging Economies: Perspectives from the Marketing Domain in India[END_REF]. Given that SCRM is a part of ERM, it is essential that any AI-based decision making complies to ERM policies and is integrated into ERM systems and processes. Based on the above factors, following hypotheses have been proposed: (H5) Awareness of AI is positively related to AI adoption intention, AI implementation intention, and AI routinization intention in SCRM, (H6) Organizational agility is positively related to AI adoption intention, AI implementation intention, and AI routinization intention in SCRM, (H7) Talent is positively related to AI adoption intention, AI implementation intention, and AI routinization intention in SCRM, (H8) Leadership support for AI is positively related to AI adoption intention, AI implementation intention, and AI routinization intention in SCRM, (H9) ERM alignment is positively related to AI adoption intention, AI implementation intention, and AI routinization intention in SCRM, (H10) Organizational size is positively related to AI adoption intention, AI implementation intention, and AI routinization intention in SCRM, and (H11) Organizational location is positively related to AI adoption intention, AI implementation intention, and AI routinization intention in SCRM.

Environmental Context

External pressure has been cited to be a significant factor in adoption research [START_REF] Hossain | Developing and validating a model explaining the assimilation process of RFID: An empirical study[END_REF] for innovative technologies. Also, past negative effects of disruptive events on the supply chain are a strong predictor of AI adoption given its potential to predict such events and its impact. Recent reports indicate a growing involvement of governments in prioritizing AI and taking proactive steps to fund AI research [START_REF] Bughin | Notes from the AI frontier: modeling the impact of AI on the world economy[END_REF]. On the other hand, government regulations on data privacy and data security could be limiting AI adoption. The following hypotheses have been proposed: (H12) External pressure is positively related to AI adoption intention, AI implementation intention, and AI routinization intention in SCRM, (H13) Disruptive event is positively related to AI adoption intention, AI implementation intention, and AI routinization intention in SCRM, and (H14) Regulation is negatively related to AI adoption intention, AI implementation intention, and AI routinization intention in SCRM.

Dependent Variables

The authors propose the following hypothesis based on the adoption stages for the dependent variables namely, the AI adoption intention, AI implementation intention, and AI routinization intention in SCRM: (H14) AI adoption intention is positively related to AI implementation intention in SCRM, and (H15) AI implementation intention is positively related to AI routinization intention in SCRM.

Implications

From a short to mid-term perspective, as businesses in India gradually emerge from a global pandemic that has devasted operations and supply chains globally, the need to adopt, implement and routinize AI in the field of SCRM is rapidly becoming a business priority. Also, the value of AI as applied to the larger supply chain risk domain will attract a lot of attention and research in the coming years. To do so, it is imperative to first understand the factors influencing the adoption, implementation, and routinization intention. The authors argue that the proposed research model based on the TOE framework with newly added factors will enable further studies and empirical research. Furthermore, this study will greatly benefit business leaders, supply chain planners, and risk professionals to devise strategies in their respective organizations to adopt, implement, and routinize AI in the field of SCRM.

Conclusion

This study provides an in-depth analysis of factors influencing AI adoption, implementation, and routinization intention. It proposes a research model that can be used for further empirical research across industries in India.

Fig. 1 .

 1 Fig. 1. Research Model