# Modified UTAUT2 to Determine Intention and Use of E-Commerce Technology Among Micro & Small Women Entrepreneurs in Jharkhand, India

Sraboni Dutta, Shradha Shivani

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Sraboni Dutta, Shradha Shivani. Modified UTAUT2 to Determine Intention and Use of E-Commerce Technology Among Micro & Small Women Entrepreneurs in Jharkhand, India. International Working Conference on Transfer and Diffusion of IT (TDIT), Dec 2020, Tiruchirappalli, India. pp.688-701, 10.1007/978-3-030-64861-9_60. hal-03744806

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# Modified UTAUT2 to determine Intention and Use of E-Commerce Technology among Micro & Small Women Entrepreneurs in Jharkhand, India

Dr. Sraboni Dutta<fnref n="1" /> and Dr. Shradha Shivani <fnref n="2" />

sdutta@bitmesra.ac.in, shraddhashivani@bitmesra.ac.in

**Abstract.** E-commerce revolution is creating enormous growth opportunities for Indian SMEs. Yet, reports suggest that adoption of e-commerce remains low among women entrepreneurs in the micro and small sector. This research applies a technology acceptance perspective to examine the determinants which induce the behavioral intention to accept and use e-commerce among the women entrepreneurs of Jharkhand, a developing state of eastern India. The UTAUT2 model of technology acceptance was modified to measure the relative impact of performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, price value, individual innovativeness and achievement motivation on their intention to adopt and use e-commerce under the influence of age and experience as moderators. Structural Equation Modeling was employed and the findings confirm the positive influence of all the core determinants except for hedonic motivation. This implies that in order to improve the weak penetration of e-commerce in the population, campaigns have to be designed and implemented to make the women entrepreneurs perceive that investment in ICT for e-commerce will be valuable for them and that they can easily use the technology.

**Keywords:** E-commerce, Modified UTAUT2, Women Micro & Small Entrepreneurs, Structural Equation Modelling.

## 1 Introduction

One of the ICT applications which have gained immense usage is e-commerce (electronic commerce). India has been a witness to an e-commerce boom. With exponential growth in internet penetration in last few decades, the e-commerce sector in India is particularly lucrative for all. It is projected that by 2020 approximately 330 million people in India will be buying goods and services online. It is projected that between 2016 and 2021, retail e-commerce will grow from around 16 billion USD to over 45 billion USD and the CAGR for the sector is projected to reach 23 percent [32].

Given this scenario, it has become imperative for SMEs to embrace digitization and adopt ecommerce to accelerate their business growth. Moreover, with the onslaught of the Covid-19 pandemic, this is fast becoming a necessity for sustenance. Numerous reports like the KPMG Snapdeal Report [36] highlighted the various benefits Indian SMEs using ecommerce have been reaping. Yet only 43% of SMEs are involved in online sales [65].

<footnote n="1">¹ Birla Institute of Technology, Mesra, Ranchi-835215</footnote>

<footnote n="2">² Birla Institute of Technology, Mesra, Ranchi-835215</footnote>
In India, where the economic contribution of women is approximately 17% of the GDP, the growth of women-run businesses could translate into a huge boost to the economy. Indian women entrepreneurs in the SME sector are increasingly coming to the forefront and are now confident and optimistic about the growth of their businesses [16].

ICT and e-commerce can help women entrepreneurs to reduce time and mobility constraints, increase market reach, upgrade skills and enable wider participation in business network [41]. Women entrepreneurs are increasingly prompted to use ICTs like mobile phones, SMS, e-mail, social network and internet based telephony (such as Skype) to reach their existing and potential customers and business partners, thereby increasing their confidence in conducting economic activities [3, 45]. Big market-places like Amazon Mahila-e-Haat, e-Bay, Alibaba.com amongst others are continuously inspiring and encouraging Indian women entrepreneurs to adopt ecommerce [36, 46, 56]. The ‘Digital India’ campaign of the Government of India is also aimed at enabling wider section of businesses to adopt ICT. Despite this, the small and micro women entrepreneurs continue to encounter barriers in incorporating ICT for ecommerce into their business firms. It has been reported that only 20 per cent of these women entrepreneurs in India use online platforms to sell [64]. This reflects the lack of infrastructure or awareness about the benefits of e-commerce platforms. Fear of security of payments and privacy of online transactions are issues plaguing women entrepreneurs. For women entrepreneurs, particularly at the bottom of the pyramid to make the most out of India's e-commerce boom, there is definitely need for mentorship, technical handholding and business advice [57].

An inherent reason for the above observations could be the gender digital divide, a characteristic of majority of the developing nations [26,49, 50, 51]. It is stated that women in South Asia (including India) are 26% less likely to own a mobile phone than men and 70% less likely to use mobile internet [57]. The “Towards Gender Equity Online” study [22] and the India Internet 2019 report [28], also states that the female internet user population in India is only half of the 258 million male internet users, and ‘the bias is more evident in rural India’. This digital divide possibly inhibits internet and ecommerce usage among women entrepreneurs as well.

In the above context arises the need for conducting a holistic study aimed at identifying and analyzing the determinants of ecommerce adoption by Indian small and micro women entrepreneurs. This research employs the extended Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) Model [68] for empirical analysis to identify the key determinants affecting the behavioral intent and ensuing usage of ecommerce by these women entrepreneurs. This model is considered to have better explanatory power than the TAM (Technology Acceptance Model) and UTAUT models that have been used to explain users’ behavioral intentions toward various information technologies [68]. Findings of this study may aid governments, international organizations, research institutions, NGOs, development banks and other stakeholders of women and entrepreneurial development in reviewing, designing and implementing gender-responsive trade and economic policies and programmes. This would also help in ensuring the success of the “Digital India” campaign of the Government of India and simultaneously enhance empowerment of women in the country.
## 2 Literature Review

E-commerce is an ICT application which has emerged as a catalyst for business growth. It is described as the “use of electronic means to conduct an organization’s business internally and/or externally” [29]. It has the potential to reduce costs, augment profitability, productivity and strengthen B2B/B2C relationships, facilitate better management of information, reposition business offerings, ensure enhanced visibility and sustain competitive advantage [6, 48, 55].

A brief discussion of some studies that have been conducted during the last few years to investigate the determinants of e-commerce adoption by SMEs is presented here. Barrosso et al., [10] developed a fuzzy cognitive map to show that *entrepreneur profile, market, operational management, marketing and promotions, website and digital platform, and products* are the most important determinants of SME e-commerce adoption. Kartiwi et al. [31] examined how the external factors such as customers and competitors drive Malaysian SMEs to adopt ecommerce and outlined the resultant benefits gained by them. Rahayu and Day [54] used the Technology, Organization, Environment (TOE) Framework to highlight that perceived benefits, technology readiness, owners’ innovativeness, owners’ IT ability and IT experience significantly influence Indonesian SMEs in adopting e-commerce. Other researchers in different nations like Garg and Choue [21], Yeh et al. [70], Klaiber et al. [35], Kenneth et al. [33], Hinson and Sorensen [27] and MacGregor [39] too have arrived at a list of different determining factors which in many cases are overlapping in nature. The level of adoption of e-commerce technologies by SMEs bears some correlation with the level of development of the nation [23, 30] and its “E-readiness”[11].

Literature reveals a range of e-business adoption and usage challenges that women managed businesses specifically in the context of developing countries are confronted with [34, 53]. These include lack of human, social, financial and physical capital culminating in diminished entrepreneurial abilities. Moreover, institutional impediments that are manifested as socio-cultural expectations regarding their commitments to family and home, alongwith existence of gender-based discrimination within the business/organizational sphere also plagues these women entrepreneurs. In addition there are individual attitudinal issues and women centric perceptions towards adoption of technology [26, 40, 66]. However, limited number of studies focuses on the drivers inducing behavioral intention to adopt and use ecommerce from the technology adoption perspective, specifically amongst women entrepreneurs of developing nations and studies in India are scant. Thus, arises the rationale of this research.

The review of literature indicated that this research required a holistic and structured model that assumes an individual-level approach of technology acceptance to examine the determinants that influence behavioral intention of women entrepreneurs to adopt and use e-commerce technology in the Indian SME context. The theoretical framework for the study is presented in the next section.

### 2.1 Theoretical Framework

The Unified Theory of Acceptance and Use of Technology Model-2 (UTAUT2) originated in the theories of Technology Acceptance Model (TAM) and thereafter
UTAUT developed by Venkatesh et al., in 2003 [67] to study the behavioral intention of people to accept and use technology. However Venkatesh et al. [68] extended the model to propose UTAUT2 in 2012, to study acceptance and use of technology in a consumer context. By introducing psychological and cognitive factors such as He donic Motivation (HM), Price Value (PV), and Habit (HT) in addition to the four core determinants, Performance Expectancy (PE), Effort Expectancy (EE), Social Influ ence (SI) and Facilitating Conditions (FC) which directly influence the user’s behav ioral intention to use new technologies. The Behavioral Intention (BI) then affects the Usage Behavior (UB). Three moderators of key relationships are Gender, Age and Experience which either amplify or constrain the influence of the core determinants, on Behavioral Intention and Usage Behavior.

It has been used by researchers to explain technology adoption in case of B2B/C2C e-marketplaces [4], mobile apps for restaurants [60], E-commerce pay-ment systems [2, 14], food delivery apps [38], online shopping [8, 62], mobile mar-keting [18], mobile wallet adoption [42], social recommender systems [52], music e-commerce [69], cloud computing in organizations [7], social commerce [5] etc. Dwivedi et al. [17] through their study using Meta-analysis methodology, proposed that attitude, partially mediated the effects of exogenous constructs on behavioral intentions, and had a direct influence on usage behaviors. Few studies employing UTAUT 2 and UTAUT have been conducted with entrepreneurs as the subjects of study. Abushakra &Nikbin [1] extended the UTAUT2 Model to empirically examine the factors guiding Acceptance and Adoption of the Internet of Things (IoT) by entrepreneurs in Oman. They posited that in addition to all the key constructs of the UTAUT 2 model, knowledge of information technology was an additional driver. Erumi-Esin and Heeks [19] used the UTAUT model to examine e-business adoption and use among African women owned SMEs and came to the conclusion that per-ceived usefulness (PE) had a greater influence than ease of use (EE) and facilitating conditions play a greater role than social influence in the adoption decision. Based on studies conducted on SMEs, few other researchers also concluded that performance expectancy is the strongest predictor of behavioral intention to use technology [47, 58].

It was concluded from the above review of literature explaining technology adoption that UTAUT 2 is a widely applicable theoretical model for an empirical study of intention and use of technology. Therefore, this was the model chosen for this research to analyze the drivers for adoption of ecommerce technology by Women Entrepreneurs in the state of Jharkhand. However, in this study an attempt has been made to modify the UTAUT 2 model to make it more relevant for the subject under consideration.

There were three modifications proposed in the UTAUT2 model. Habit as a determinant of behavioural intention was excluded and two psychological characteris-tics i.e. Innovativeness and Achievement Motivation were included. Finding from depth interviews conducted with few women entrepreneurs from the population of the study and with experts during the exploratory study suggested that majority of these women entrepreneurs are not habituated to use ecommerce repeatedly. Hence habit as
a driver would be redundant for the study. The psychological traits of Innovativeness and achievement orientation included in the model have long been considered as drivers of entrepreneurial success. Many recent studies have also focused on these psychological traits as related with entrepreneurship [20, 44, 61, 63]. Since the population of this research consisted of women entrepreneurs and the focus of the research was adoption of new technology (ecommerce) of conducting business for achieving enterprise success, therefore these two entrepreneurial traits were included as drivers of BI. In line with the moderating impact of Age and Experience on the influence of the drivers of intention to use technology as depicted in UTAUT2 model, it is proposed in this study that these variables will moderate the influence of individual innovativeness and achievement motivation on behavioural intention of the entrepreneurs to use ecommerce. Table 1 provides a summary of each of the modified UTAUT2 determinants, (core constructs) and dependent variables along with its description. Figure 1 depicts the conceptual model of this study.

<table><caption>**Table 1.** Modified UTAUT2 (Venkatesh et al., 2012) variables</caption><thead><tr><th colspan="2">Core Determinants</th></tr><tr><th>Construct</th><th>Definition</th></tr></thead><tbody><tr><td>Performance Expectancy (PE)</td><td>The degree to which an individual believes that using ecommerce will help him/her to attain gains in job performance</td></tr><tr><td>Effort Expectancy (EE)</td><td>The degree of ease associated with the use of ecommerce</td></tr><tr><td>Social Influence (SI)</td><td>The degree to which an individual perceives that important others believe that he/she should use ecommerce</td></tr><tr><td>Facilitating Conditions (FC)</td><td>The degree to which an individual believes that an organizational and technical infrastructure exists to support the use of ecommerce</td></tr><tr><td>Hedonic Motivation (HM)</td><td>The fun, pleasure (in context of status) derived from using ecommerce</td></tr><tr><td>Price Value (PV)</td><td>Entrepreneurs' cognitive tradeoff between the perceived benefits of ecommerce and the monetary cost involved</td></tr><tr><td>Individual Innovativeness (IN)</td><td>Extent to which entrepreneur is open to experiencing, and experimenting with, new technologies involved in ecommerce</td></tr><tr><td>Achievement Motivation (AM)</td><td>Degree to which the entrepreneur rates the positive or negative affect towards an achievement activity</td></tr></tbody><thead><tr><th colspan="2">Dependent Variables</th></tr><tr><th>Construct</th><th>Definition</th></tr></thead><tbody><tr><td>Behavioural Intention (BI)</td><td>The intention of the individual to use the system</td></tr></tbody></table>
<table><tr><td>Usage Behaviour(UB)</td><td>The extent of usage of the system by the individual</td></tr></table>

<figure><img src="image_2.png" /><figcaption>Fig. 1. Modified UTAUT2 Model, (Venkatesh et al., 2012)</figcaption></figure>

## 2.2 Research Hypotheses

Based on the modified UTAUT2 Model which forms the theoretical framework presented in the previous section the following hypothesis were formulated for the study:

H₁: Performance expectancy positively influences intention to use e-commerce

H₂: Effort expectancy positively influences intention to use e-commerce

H₃: Social influence positively influences intention to use e-commerce

H₄: Facilitating conditions positively influences intention to use e-commerce

H₅: Hedonic motivation positively influences individual intention to use e-commerce

H₆: Price value positively influences individual intention to use e-commerce.

H₇: Individual's innovativeness positively influences intention to use e-commerce

H₈: Individual's achievement motivation positively influences intention to use e-commerce

H₉: Facilitating conditions positively influence the individual's e-commerce usage behavior

H₁₀: Intention to use ecommerce positively influences the individual e-commerce usage behaviour

H₁₁: The influence of performance expectancy on behavioral intention is moderated by age.
H₁₂: The influence of effort expectancy on behavioral intention is moderated by age.

H₁₃: The influence of effort expectancy on behavioral intention is moderated by experience.

H₁₄: The influence of social influence on behavioral intention is moderated by age.

H₁₅: The influence of social influence on behavioral intention is moderated by experience.

H₁₆: The influence of facilitating conditions on behavioral intention is moderated by age.

H₁₇: The influence of facilitating conditions on behavioral intention is moderated by experience.

H₁₈: The influence of hedonic motivation on behavioral intention is moderated by age.

H₁₉: The influence of hedonic motivation on behavioral intention is moderated by experience.

H₂₀: The influence of price value on behavioral intention is moderated by age.

H₂₁: The influence of price value on behavioral intention is moderated by experience.

H₂₂: The influence of individual's innovativeness on behavioral intention is moderated by age.

H₂₃: The influence of individual's innovativeness on behavioral intention is moderated by experience.

H₂₄: The influence of individual's achievement motivation on behavioral intention is moderated by age.

H₂₅: The influence of individual's achievement motivation on behavioral intention is moderated by experience.

H₂₆: The influence of facilitating conditions on use behavior is moderated by age.

H₂₇: The influence of facilitating conditions on use behavior is moderated by experience.

H₂₈: The influence of behavioural intention on use behavior is moderated by experience.

### 3. Research Methodology

The study is conducted amongst the small and micro women entrepreneurs in the state of Jharkhand in eastern India. The study has modified the UTAUT2 model of Venkatesh et al.[68], and theorizes that performance expectancy (PE), effort expectancy (EE), social influence (SI), and facilitating conditions (FC) hedonic motivation (HM), price value(PV), individual innovativeness (IN) and achievement motivation (AM) are the core determinants of women entrepreneurs' intention to adopt and use e-commerce moderators in the model being age and experience. The purpose of this study was to determine the strength of these determinants (PE, EE, FC and SI, HM, PV, IN, AM) on behavioral intention to use ecommerce (BI) and usage behavior (UB)
in light of the moderating effects of age and experience in using technology. The
moderator, gender has been excluded in this study.

**3.1 Data collection and Data analysis**

The database on women entrepreneurs were collected from the District Industry Cen-
tres (DIC) of Bokaro, Ranchi and Jhamshedpur. A structured questionnaire was ad-
ministered among a sample of 300 micro and small women entrepreneurs. Valid
responses were received from 258 women- response rate of 86%. The first section of
questionnaire captured the demographic profile of the respondents, their usage of
various ICT tools and their extent of ecommerce adoption as per “hierarchical phases
of e-commerce adoption” as indicated by Molla and Licker [43]. The second section
captured their perceptions regarding the adoption of e-commerce on the basis of the
modified UTAUT2 Model, using a 5-point Likert Scale (1-Strongly Disagree; 5-
Strongly Agree). The items used by Venkatesh et al. [68] were replicated. For solicit-
ing responses on individual innovativeness, a 3 item scale was adopted from Slade et.
al [59] to assess the willingness of the women entrepreneur to try out e-commerce
technologies. Responses on achievement motivation were collected via the 10 item
Revised Achievement motivation Scale (AMS-R) designed by Lang & Fries [37] to
gauge how these women rate their positive or negative affect towards an achievement
activity like adopting ecommerce for business sustenance and growth. SPSS 22.0 and
AMOS were the statistical packages used to analyze the data.

4 Analysis

The demographic profile of the respondents indicate that majority of these entrepre-
neurs is in the age group of 30-45 years and has graduated from college (55.7%). The
predominant form of activity is manufacturing (70%) and sole proprietorship is the
main form of ownership (81%) in these firms.

The study revealed that usage of ICT was prevalent amongst all the sampled wom-
en entrepreneurs in some form or the other. It varied from usage of smart phones,
SMS, emails, social media and skype to employing financial accounting packages or
hosting a website. With respect to adoption of ecommerce, approximately 28% uti-
lized the Connected E-commerce phase in which the firms were using e-mail to sup-
port information and transactional processes with existing and potential trading part-
ners, suppliers and customers. Merely 14% of them were hosting their own website
for information sharing and even lesser percentage (10%) had adopted the phase of
Interactive Ecommerce, thereby facilitating online interactions and queries between
the firm and its customers. A very small fraction of these women entrepreneurs (5%)
had adopted Transactive Ecommerce, allowing customers to purchase online, track
orders, and manage their account information. None of these entrepreneurs had ven-
tured into the scheme of Integrated Ecommerce in which the firm's website is inte-
grated with suppliers, customers, and other back-office systems allowing most busi-
ness transactions and processes to be conducted electronically. The descriptive statis-
tics for the constructs used in this study reveal that except for Usage Behaviour, the
mean values of all constructs exceed 3, indicating that most of the respondents agreed
or strongly agreed with the statements in the questionnaire.
## 4.1 Analysis of measurement model

Structural equation modeling was undertaken to analyze the data. Hair et al. [24] suggests that factor loadings must be at least 0.5 and preferably greater than .7 for an acceptable measurement indicator. The construct reliability (CR) must be greater than 0.7 and average variance extracted (AVE) for each construct should be greater or equal to 0.5. There is evidence that the measurement model has adequate reliability and convergent validity because all factor loadings were greater than 0.7, the CRs exceeded acceptable criteria of 0.7, and the AVEs were greater than the threshold value of 0.5 in all cases

Table 2 provides a list of different indices for structural model fit, recommended threshold values, along with corresponding literature and the results obtained. It is evident that the results satisfy the threshold levels and hence it implies that measurement model fits the data collected.

<table><caption>Table 2. Criteria for Model Fit</caption><thead><tr><td>Test</td><td>Recommended value</td><td>Authors</td><td>Results</td></tr></thead><tbody><tr><td>Root mean square error of approximation (RMSEA)</td><td>&lt;.08</td><td>Browne and Cudeck [12]</td><td>&lt;.0394</td></tr><tr><td>Goodness-of-fit statistic (GFI)</td><td>&gt;=.90</td><td>Hair et al. [25]</td><td>.93</td></tr><tr><td>Adjusted goodness-of-fit statistic (AGFI)</td><td>&gt;.80</td><td>Chau and Hu [13]</td><td>.90</td></tr><tr><td>Normed-fit index (NFI)</td><td>&gt;=.9</td><td>Chin and Todd, [15]; Hair et al. [25]</td><td>.95</td></tr><tr><td>CFI (Comparative fit index)</td><td>&gt;=.9</td><td>Bagozzi and Yi, [9]</td><td>.94</td></tr></tbody></table>

<table><caption>Table 3. Relationship between the Core Determinants and the Dependent Variables.</caption><thead><tr><th colspan="3">Path</th><th>Beta</th><th>t value</th><th>p</th><th>Decision</th></tr></thead><tbody><tr><td>BI</td><td>←</td><td>PE</td><td>0.789</td><td>10.066 ***</td><td>0.000</td><td>supported H1</td></tr><tr><td>BI</td><td>←</td><td>EE</td><td>0.696</td><td>10.266 ***</td><td>0.000</td><td>supported H2</td></tr><tr><td>BI</td><td>←</td><td>SI</td><td>0.134</td><td>2.417 *</td><td>0.016</td><td>supported H3</td></tr><tr><td>BI</td><td>←</td><td>FC</td><td>0.229</td><td>2.994 **</td><td>0.003</td><td>supported H4</td></tr><tr><td>BI</td><td>←</td><td>HM</td><td>0.096</td><td>0.766</td><td>0.443</td><td>rejected H5</td></tr><tr><td>BI</td><td>←</td><td>PV</td><td>0.25</td><td>3.45**</td><td>0.003</td><td>supported H6</td></tr></tbody></table>
<table><tr><td>BI</td><td>←</td><td>IN</td><td>0.24</td><td>2.56*</td><td>0.031</td><td>Supported H7</td></tr><tr><td>BI</td><td>←</td><td>AM</td><td>0.530</td><td>9.788 ***</td><td>0.000</td><td>supported H8</td></tr><tr><td>UB</td><td>←</td><td>FC</td><td>0.462</td><td>4.593***</td><td>0.000</td><td>Supported H9</td></tr><tr><td>UB</td><td>←</td><td>BI</td><td>0.212</td><td>2.136*</td><td>0.033</td><td>Supported H10</td></tr></table>

Note: *p < 0.05; **p < 0.01; ***p < 0.001

## 5. Findings & Discussion

The results of this study are consistent with the original postulations of Venkatesh et al. [67] regarding influence of the four core determinants of intention and usage but they do not fully support the postulations regarding the effect of moderators.

As is evident from results presented in Table 3 the core determinants i.e. performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC), price value (PV), individual innovativeness (IN), achievement motivation (AM) have a significant positive influence on behavioral intention (BI). Among the determinant variables, PE is found to have the strongest influence of BI. The results further suggested that BI and improved facilitating conditions (FC) are significantly influencing usage behavior (UB) for ecommerce adoption by women entrepreneurs in a positive manner. Thus, the results support the H₁, H₂, H₃, H₄, H₆, H₇, H₈, H₉, and H₁₀ hypotheses.

The results from the moderator analysis indicate that the age of the entrepreneur does not moderate the effect of PE, EE, SI, FC, HM, PV, IN and AM on BI. Perception about effect of these core determinants on BI is similar across all age groups. Similarly, experience does not moderate the effect of EE, FC, HM, PV, IN and AM on BI. Experience also does not moderate the effect of FC and BI on UB. Hence H₁₁, H₁₂, H₁₃, H₁₄, H₁₆, H₁₇, H₁₈, H₁₉, H₂₀, H₂₁, H₂₂, H₂₃, H₂₄, H₂₅, H₂₆, H₂₇ and H₂₈ are not supported. However, the results indicate that experience significantly moderates the effect of SI on BI, thus, supporting H₁₅ hypothesis indicating that influence of peer members, associations, friends and business partners on intention to use technology will be stronger on women entrepreneurs who have the experience of using ICT in other walks of life.

Consistent with the results of moderator analysis, with respect to the direct influence of the moderators on the dependent variables it is observed that experience is significantly influencing the usage behavior in a positive manner. The direct effect of moderator age on the two dependent variables is found to be 'not significant'. From the results obtained we posit that age of the women entrepreneurs is no longer a relevant factor impacting technology acceptance by them. Although the overall penetration of ecommerce is low among the women entrepreneurs, the strength of intention to use ecommerce and extent of usage is spread evenly across women entrepreneurs
of all age groups. It may be concluded that the impact of digitization awareness campaigns by various agencies has been uniform across women in all age groups.

**6. Conclusion**

This study takes a technology acceptance perspective towards investigating the drivers of ecommerce adoption among women entrepreneurs in the small and micro sector operating in Jharkhand, India. The theoretical significance of this research is derived from the fact that it presents fresh empirical findings based on application of the widely used UTAUT2 model for predicting technology acceptance and use by individuals. The model was tested on a very specific target group of women entrepreneurs in diverse locations of a developing state in India. The results support the relationships between the core determinants of technology acceptance and the intention to use technology proposed in the model.

The findings of this study have many managerial and policy implications. Despite the thrust of the government and non-government agencies in India towards promotion of women entrepreneurs in the country, they do not seem to have reaped the benefits of the ecommerce boom in India. It has been reported that only 20 per cent of the women entrepreneurs in India use online platforms to sell [65]. Less than 30% of the women owning small and micro enterprises who were interviewed in this study were found to have adopted the rudimentary ecommerce processes. This highlights the need for interventions which can address the technology acceptance issues specific to such women enterprises in a developing nation context. This becomes all the more relevant in this Covid-19 pandemic situation where dependence on the virtual platform is imperative. Based on the empirical findings of this study various policy interventions can be forwarded

PE being the strongest predictor of ecommerce usage intention indicates that if interventions are directed towards inducing women entrepreneurs to perceive that using e-commerce applications would improve the performance of their businesses in terms of growth, efficiency and productivity, the intention to adopt such applications could be enhanced. The business benefits of ecommerce applications need to be highlighted amongst the women. This could be supplemented with success stories that they can relate to.

The statistically significant influence of EE suggests that the women entrepreneurs need to be convinced regarding the simplicity and ease of use of such technology. Workshops and training modules can be instrumental in building their confidence to handle ecommerce applications. The findings of this study also indicate SI via social networks of friends, family, customers, business partners, bankers is an important driver for the behavioral intent to adopt this technology. SI exerts a statistically significant influence on women entrepreneurs' intention to use e-commerce. Social awareness campaigns highlighting that investment in ICT for ecommerce is imperative for the success of women entrepreneurs can be utilized to strengthen the social influence.

FC too emerged as a strong driver for both intention to use and usage behavior, suggesting that agencies responsible for promoting women entrepreneurship need
to stay focused on providing necessary resources and infrastructure for encouraging
the women entrepreneurs to use e-commerce applications. Since, PV has a positive
significant influence on behavioral intention to accept ecommerce, support agencies
via campaigns can attempt to convince these women that long term business benefits
of ecommerce adoption would outweigh the monetary costs. Entrepreneurship devel-
opment programmes should continue to inculcate and enhance the entrepreneurial
personality traits like innovativeness (IN) and achievement orientation (AM) amongst
these women entrepreneurs so as to induce stronger intention to use ecommerce.

The interventions by institutions, both Government and private, need to
reach a wider section of women owners of small and micro enterprises in the interiors
of the country. Schemes which offer financial and technical assistance to new entrants
to incorporate ecommerce and other technologies in their business firms, can be rede-
signed especially for women owned small and micro enterprises. Such schemes which
solely target this segment can help the women entrepreneurs to overcome the digital
divide which still persists in various locations. Information regarding availability of
affordable ICT infrastructure and connectivity needs to be disseminated and high-
lighted in this target group. Widespread awareness building and motivational cam-
paigns to reduce the attitudinal barriers to ecommerce usage can help to ensure large
scale and effective adoption of these applications by the women entrepreneurs.

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