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ELK ASIA PACIFIC JOURNAL OF MARKETING AND RETAIL MANAGEMENT-SPECIAL ISSUE ISSN 0976-7193 (Print) ISSN 2349-2317 (Online); DOI: 10.16962/EAPJMRM/ISSN.2349-2317/2014

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COMPARATIVE STUDY OF TECHNOLOGY ACCEPTANCE AND PRIVACY CONCERNS BETWEEN USERS AND NON-USERS OF VIRTUAL ASSISTANT DEVICES.

Dr. Deepika Dabke

Associate Dean – ICFAI Business School, Mumbai

Prof. Priyanka Mathur Dhingra Research Scholar, ICFAI University

ABSTRACT

Technology and Artificial Intelligence (AI) have found a unique place in the lives of consumers today. From personalizing advertisements for consumers, based on their browsing history to predicting their candidate preferences during elections, AI’s poses to be an omniscient, omnipotent and all-pervasive phenomena of the new era. Virtual assistants (VAs), also known as digital assistants or personal digital devices, is one such wave from the world of Internet of Things (IoT) that offers consumers numerable possibilities of smart living. While use of VAs is on a high, this study is an attempt at understanding the perceptual differences between users and non-users of VAs, with specific focus on Technological Adoption and privacy concerns. 211 respondents were approached through a 29-item online a survey form that examined the Perceived ease of use, Perceived usefulness, Attitude towards VA technology, Perceived Intrusion, Privacy concerns and Usage Intention of users and non-users. An independent samples t test results indicated that VA users scores were significantly higher than non-users on Perceived ease of use, Perceived usefulness, Attitude towards VA technology Usage Intention as compared to non-users. As expected, User scores on Privacy concerns were significantly lower than Non-users. There was a negligible difference in the scores of users and non-users on perceived intrusion. A frequency analysis of purpose of use of VAs in Users showed that, VA technology is mainly used for seeking information, entertainment and accomplishing routine tasks. On the other hand, major barriers in the use of VAs in non-user’s view were privacy concerns, technological hassles and fear of misuse of information. The findings have been discussed with respect to their implications for marketeers and promoters and advocates of use of VA technology.

KEYWORDS-Virtual Assistants, Digital Assistants, Technological Adoption Model (TAM), Privacy concerns, Perceived Ease of Use, Perceived Usefulness, Attitude, Hedonic motives, Perceived Intrusion, Behavioral Intention

Introduction The adoption of new technology based Virtual assistants (VAs) is accelerating. The number of people using virtual assistants has been growing exponentially and is expected to grow from 390 million in 2015 to 1.8 billion in 2021 worldwide. The meteoric rise of VAs is being fuelled by advances in technologies such as

natural language processing (NLP), artificial intelligence (AI) and machine learning, supported by high-speed Internet connectivity and cloud computing. (Cognizant 20-20 insights, 2017). Virtual assistants (VAs) are software agents capable of performing tasks with minimum guidance from its users. They


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can interpret human speech and respond via synthesized voices. These are also being addressed as Intelligent Personal Assistants, Intelligent Digital Assistants, Chatbots, Conversational agents or Smart Personal Assistants. VAs combine artificial intelligence technologies to assist users, to organize information and to adapt to changing situations (Lamontagne, et al. 2014). There are four major categories of Virtual Assistants on the basis of how they integrated into devices – smart speakers such as Amazon Echo, VAs built into mobile operating systems such as Apple's Siri, third are the VAs built into a desktop operating systems such as Cortana on Microsoft Windows and lastly those which are built into a smartphone independent of the OS such as is Bixby on Samsung Galaxy S8 and Note 8. They are be further classified on the basis of the nature of commands on which they run - (1) ‘Text messaging’ (through online chats), (2) ‘Voice’ (with Amazon Alexa on the Echo device, Siri on iPhone, and Google Assistant on Android devices and (3) ‘images’ (on Samsung Bixby). Using AI technology, these devices have the capacity to learn an individual’s preferences and can provide customized services without any active user input. (Han & Yang, 2018). They can not only make dinner reservations, play the users favorite music, and get a weather update, but also control their home appliances and ensure the security of their homes. Apple’s Siri, Amazon’s Alexa, Microsoft’s Cortana, and Google’s Assistant have found wide acceptance in the areas of education, medical assistance, robotics, entertainment, home automation and

security. VAs are a product of the Web 4.0, also known as symbiotic web. The goal of the symbiotic web is interaction between humans and machines in symbiosis (Solanki & Dongaonkar 2016) VAs have created an ecosystem where a user can talk to a machine as if it were a human assistant and the machine helps improve quality of life and productivity. But resultant to this interaction, VAs collect vast information about lifestyle of users and could potentially create an opportunity for third parties to covertly listen into and intrude on private conversations. This paper endeavors to study the privacy concerns and technological adoption related with VAs . The motivation for this research is the enigma around the acceptance of VAs as a part of consumers’ lifestyle that can explain customers’ behavioral intentions to use or not use VAs with respect to the Technology Acceptance model (TAM). There is a dearth of empirical research on the adoption, uses and challenges of these devices in Indian and the implications of interactions with them in terms of loss of privacy. This study investigates how people perceive, interact with, and integrate these devices into their social life and will explore the challenges around VA acceptance, primarily the privacy concerns of users. The main objectives of the study were

To study technology adoption differences between users and non users

To examine privacy concerns between users and non users



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Literature review Virtual Assistants like Amazon’s Alexa, Microsoft’s Cortana, Google’s Assistant, and Apple’s Siri possess conversational and language-understanding skills to help users accomplish a range of tasks, from reminder and recreation to home security. Niewiadomski et al. (2010) described the characteristics that virtual assistants should display to elicit social reactions of users toward the agent. They revealed that socially appropriate emotions lead to higher perceived believability of VAs. To create a believable VA, its emotional (verbal & nonverbal) behavior should be socially adapted. Their research suggested that emotional agents are judged more believable than non emotional one. Two main socio-cognitive factors - warmth and competence increase the perception of believability of VAs and make them more acceptable to users. Trust and acceptability are key to product adoption. In a similar study on Virtual try-on technology, in the context to online shopping Kim & Forsythe (2008) found that interactivity and customer involvement enhances the entertainment value of the shopping experience. They found innovativeness and technology anxiety also strongly influenced adoption. In existing literature (Silva & Dias 2007), several theories are detailed which forecast the impact of technology on user adoption – non adoption behaviour such as Theory of Reasoned Action (TRA), Theory of Planned Behaviour (TPB) and various forms of Technology Acceptance Model (TAM). In this paper we used the classic TAM to study the impact of privacy concerns on acceptance or non

acceptance of a technology product like Virtual Assistants. In conformance with extant studies, perceived usefulness (PU) and perceived ease of use (PEOU) were found to be the leading determinants of usage. Liang & Byrd (2003) studied the extended technology acceptance model (TAM) on Personal Digital Assistant (PDA) usage in healthcare. New factors - personal innovativeness, job relevance & compatibility emerged which affect the usage through perceived ease of use. Perceived usefulness was found to moderate the impact of job relevance & compatibility and perceived ease of use on Usage. However literature (Elle et al.1991) also points to attributes that lead to resistance of the adoption process of technological innovations. Studies suggest that self efficacy and a user’s level of satisfaction experienced with an existing product increases resistance to and reduces likelihood of adopting an alternative. The main attribute that leads to avoidance of adoption of technological assistances turned out to be the privacy concerns of users. Privacy related concerns have become a subject of interest for practitioners and users due to its wide implications on data security. Dinev et al. (2008) studied the fact that the Internet lends itself to an online setting of unscrupulous and potentially harmful activities, leading the government, specially the FBI’s cybercrime cell, to do extensive Internet surveillance and intelligence gathering at both vendor and online service provider levels. Their study



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focused on the Internet users’ responses to government initiatives intended to address the perceived threats to online security. The seriousness of the security threats would seem to makes such surveillance a welcome practice however public opinion reflect a society in which privacy is highly valued. Users are worried about the potential misuse of their personal information. Hypotheses On the basis of association between TAM, privacy concerns and VA usage, the following hypotheses were formulated: H1: VA users’ Perception of ease of use is significantly higher than non-users. H2: VA users’ Perception of Usefulness is significantly higher than non-users. H3: VA users’ Attitude towards VA usage is significantly higher than non-users. H4: VA users’ Hedonic motive is significantly higher than non-users. H5: VA non-users’ Perceived intrusion is significantly higher than users. H6: VA non-users’ Privacy concerns is significantly higher than users. H7: VA users’ Intention to use is significantly higher than non-users. Research methodology Sample The sample of the study comprised of 211 respondent identified through snowballing technique. A structured questionnaire was created and mailed to the respondents. The mean age of the participants was 27.436 years (S. D = 8.771). 105 males and 106 females participated in the study, out of which 56.4 % (119) were active users of

Virtual Assistants and 43.6 (92) were non-users. Measures and Procedure The questionnaire comprised of two sections. Section one sought information on the study variables and demographic variables such as age, gender, educational background and VA usage. Reponses to the 29-items questionnaire were sought on a five-point Likert type scale ranging from 1 (Strongly Disagree) to 5 (Strongly Agree). The questionaaire assessed respondent’s Perceived ease of use, Perceived usefulness, Attitude, Hedonic motive, Privacy concern Perceived Intrusiveness and Intention to use a VA. Perceived ease of use was defined as “the degree to which an individual believes that using VAs would be free of effort” and measured with four items adapted from Venkatesh and Davis (1996). Perceived Usefulness (PU) is defined as the degree to which an individual believes that VA usage will enhance their job performance (Davis et al. 1989). Attitude is the degree of affect, behavior and cognition that the respondents possess towards VA usage and is adapted from Venkatesh and Davis’s (1996) original scale. Hedonic motive is defined as, “the fun or pleasure derived from using a technology”, (Brown and Venkatesh 2005). It was measured by 3 items adapted from the original scale of Venkatesh et al. (2012). Privacy concern was measured by 7 items adapted from Luna Cortes and Vela’s (2013) scale. Perceived Intrusion scale was adapted from the work of Xu et al (2008) and comprised of 3 items. Intention to use was



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measured by a 4-item scale adapted from Venkatesh and Davis (1996). The details of the measurement tool are presented along with their reliabilities in table 4. Depending on the respondents’ answer on whether they used VA or not, they were directed to the second session. In case of users, an enquiry was made with regards to usage pattern and reason for usage. In case of non-users, an enquiry was made regarding reasons for non-usage. The participants were sent an online questionnaire. Statistical analysis Frequency distribution analysis was used to study the demographic profile of the respondents. Additionally, Frequency distribution analysis was also used to assess the reasons and barriers to VA usage and non-usage respectively. Reliability analysis for the scales was assessed by calculating the Cronbach’s Alpha. An Independent samples t test was conducted to compare means of users and non-users on variables under consideration. Results Frequency distribution analysis was conducted to assess the demographic profiles. The findings have been summarized in Table 1. The results showed that 50.2 % of the respondents were males and 49.8 % were females. The respondents were categorized in to 5 levels of age groups. As seen in Table 1, majority of the respondents fell the age range of 21-25 years (55.8%). This was followed by more than 35 years

(19.5%) and then 26-30 (14.3%). Most of the respondents had a minimum post graduate degree (65.9%). 56.4 % of the respondents were active users of VA while 43.6 % did not use any form of VA. 50.2% of the sample were males and 49.8 were females. An enquiry regarding the purpose for which VA users used the device led to a frequency analysis as indicated in Table 2. The top three purposes for the use of VAs was seeking information (80.2%), Entertainment (70.2%) and completing routine tasks such as bookings, reminders (51.2%). The bottom 2 purposes were monitoring purposes (11.6 %) and seeking companionship (6.6%). Similarly, an examination of the barriers or deterrent for the use of VAs in case of non-users is summarized in Table 3. It was found that this top three barriers identified included Privacy concerns (50%), Technological hassles (27.6%) and Fear of misuse of the features (26.2%). The reliability coefficients of the scales are presented in Table 4. All the scale reliabilities were above the prescribed value of 0.7 (Nunnally, 1978). The mean scores of users and non-users on various variables under consideration are presented in Table 5. As seen in Table 5, Users scored higher on Perceived ease of use, Perceived Usefulness, Attitude, Hedonic motive and Intention to use as compared to Non-users. As expected, their scores were lower than non-users in case of privacy concerns. In case of Perceived intrusion, there was a very negligible difference between the two groups under consideration.



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An Independent samples t test was conducted to see if the mean difference between users and non-users is statistically significant. Table 6 presents the independent samples t test scores and significance levels of the scores. As seen in the table 6, there was a significant difference between users and non-users in the perceived ease of use (t=2.505, p=0.013), perceived usefulness (t=3.171, p=0.002) , attitude (t=2.199, p=0.029), Hedonic motive (t=1.996, p=0.047), Privacy concerns (t=-1.675, p=0.051) and Intention to use (t=5.917, p=0.0001) yielding to the acceptance of H1,2,3,4, 6 and 7. The difference in the mean scores for perceived intrusion (t=0.292, p=0.77, ns) was not significantly different for users and non-users. Thus, H5 was rejected. The findings of the study are found to be as expected and are in line with the previous related studies in the field of Technology Adoption and Internet of Things (IoT). Discussion The study was an attempt to empirically test the differences between users and non-users in terms of Perceived ease of use, Perceived usefulness, Attitude, Hedonic motive, Perceived intrusion and Privacy concerns. While popular literature constantly emphasizes on the association between VA usage intention and the Technological Acceptance and Privacy concerns in people, there is a dearth of empirical studies in this context. This study was a humble attempt to bridge this knowledge gap by specifically studying the mind set of Indian population

with respect to Virtual Assistant usage. The comparative analysis between users and non-users is primarily to identify critical factors that may be responsible in intention to use VAs. It is imperative to study users and non-users because often times the two groups are very different primarily due to their perceptual and intrinsic attributes. Looking at only success stories and views of users and early adopters can acquaint marketeers with only one side of the coin. As highlighted by Sääksjärvi and Morel (2010), consumer’s doubts can also cause an innovation to fail. Thus, the current study provides vital insights to understand behvaioral differences between users and non-users. The findings bring forth the strong association between Technology Acceptance Model and Virtual Assistant usage. Perceived ease, Usefulness, Attitude are definite factors that differentiate users and non-users. Technology Adoption readiness has shown to impact consumers decision towards online shopping, marketing and e-commerce (Moriuchi & Takahashi, 2018; Pavlou, 2003), e-learning continuance intention (Roca, Chiu and Martinez, 2006), Internet banking (Yaghoubi, 2010) and virtual try-on apps (Rese et al, 2017). The current study also highlights the need for considering TAM while strategizing for promotion and advertising for Virtual Assistants. Hedonic motive has also emerged as a key factor of difference between users and non-users. This is in line with past research (Brown and Venkatesh, 2005; Thong, Hong and Tam, 2006) that



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indicate, “the fun or pleasure derived from using a technology” (Venkatesh et al 2012, p 161) can be a key factor in influencing behavioural intention of the consumer. Highlighting the element of fun, excitement and entertainment in promotional material and advertisements can greatly enhance the appeal of VAs to a wide spectrum of consumers. Data security also has emerged as an important point of differentiation between users and non-users. Martin, Borah and Palmatier (2017) have suggested that companies should adopt a more empathic approach towards customer data privacy concerns. Marketeers need to promote a sense of transparency and greater control in the hands of consumers in matters of data usage and confidentiality. Such explicit measures will go a long way to create a sense of trust with companies promoting the modern-day web 4.0 services and apps. Other researchers have also highlighted that data sensitivity can become a barrier with respect to technology adoption (Oliviera, Thomas, Baptista and Compo, 2016; Pavlov, Liang and Xue, 2007). The constant “listening” nature of Alexa have huge implications such as data misuse, access of data by stakeholders such as law enforcement agencies, in context of pinning down a suspect, marketeers, family and friends who may get privy to personal preferences and information. All in all, the study provides valuable insights on the nature of perceptual differences exhibited by users and non-users of VAs.

Theoretical contribution Till date very little is known about consumer preferences and VA usage. There has been a dearth of empirical studies trying to assess the differences between users and non-users with respect to VA usage. The study is an attempt to build a body of knowledge that can fill this gap. Davis et al (1989) have postulated that TAM explains a 40% variance in usage behavior. The major factors that influence purchase intention and usage intention are Perceived ease of use and Perceived usefulness. This study provides an empirical support to this postulation. Furthermore, the study clearly highlights the issue of data sensitivity and privacy concerns that are likely to linger in the minds of at least some potential users and thereby threatening to dampen the spirits of commercial proponents and advocates of VA technology. Managerial implications The VA usage market is comparatively a niche market, with a great scope of penetration in the near future. The current study provides marketeers meaningful insights that can help them devices their marketing strategies in a more appropriate manner. Verdegem and De Marez (2011) have opined that often times, companies and brands highlight product features without really understanding the differences between early adopters and late adopters. However, an understanding of how adopters are different from non-adopters will help companies penetrate the markets and appeal to consumers more effectively. Some of the implications of the current study are as follows:



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Focusing on enhancing the perceptions of ease of use, usefulness can greatly improve consumer attitude towards VA usage and influence behavioral intentions to use VAs in near future

Emphasizing the element of fun and excitement and building advertising brief around Hedonic motive can catch the interest of consumers and push them towards a better and faster adoption of VA technology

While AI based home security and smart home solutions seem to be on a rise, the Indian consumer does not seem to be looking at adoption of such options very easily. Home Security solution companies need to create a greater awareness and appeal of such usage before they can think of capturing the Indian market.

Interestingly, some companies try to humanize AI technology and provide humanly attributes to VAs. However, currently Anthropomorphism, a tendency to attach higher human attributes to technological devices, does not seem to work for the Indian consumer.

Finally, data security and privacy concerns are hard challenges staring at marketeers today. Companies should go all out to build safety walls, bestow greater control on consumers over data usage decisions before personalizing advertisements for them and preserve consumers related data from misuse. Limitations and way forward One of the limitations of the current study has been an enquiry in to general VA usage rather than enquiry about specific actions such as ordering on merchandise,

performing specific tasks and accessing specific product/service information. Another limitation stems from the fact that the findings are based on a sample that comprises of 58 % of population that represents respondents from the age group of 21 to 25 years. Thus, the generalizability of the results can be limited to Gen Z and millenials more that the general population. The survey method also leaves a lot to desire in terms of richness of qualitative data and insights. Future research should focus on combining a mixed method that includes both quantitative and qualitative methodologies. Conclusion The current study was an attempt at analysing differences between users and non-users in terms of Technology Acceptance and Privacy Concerns. Insights of the study can be valuable for marketeers to design effective strategies and communication plans with consumers to promote enhanced usage of VAs. References 1. Apthorpe, N., Reisman, D., & Feamster, N. (2016). Poster: A Smart Home is No Castle: Privacy Vulnerabilities of Encrypted IoT Traffic. 2. Benbasat & Wang (2005). Trust in and adoption of online recommendation agents. Journal of the association for information systems, 6(3), 4. 3. Brown, S. & Venkatesh, V. (2005). A Model of Adoption of Technology in the Household: A Baseline Model Test and Extension Incorporating Household Life Cycle. Management Information Systems Quarterly, vol. 29, no. 3, pp.399-426



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4. Bugeja, J., Jacobsson, A., & Davidsson, P. (2016, August). On privacy and security challenges in smart connected homes. In 2016 European Intelligence and Security Informatics Conference (EISIC) (pp. 172-175). IEEE. 5. Chung, H., & Lee, S. (2018). Intelligent Virtual Assistant knows Your Life. arXiv preprint arXiv:1803.00466. 6. Chung, H., Iorga, M., Voas, J., & Lee, S. (2017). Alexa, can I trust you?. Computer, 50(9), 100-104. 7. Dale, R. (2016). The return of the chatbots. Natural Language Engineering, 22(5), 811-817. 8. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003. https://doi.org/10.1287/mnsc. 35.8.982 9. Davis, F.D. (1989), "Perceived usefulness, perceived easy of use, and user acceptance of information technology", MIS Quarterly, September, pp. 319-40 10. Davis, F.D. and Venkatesh, V. (1996), "A critical assessment of potential measurement biases in the technology acceptance model: three experiments", International Journal of Human-Computer Studies, Vol. 45, pp. 19-45. 11. Dinev, T., Hart, P., & Mullen, M. R. (2008). Internet privacy concerns and beliefs about government surveillance–An empirical investigation. The Journal of Strategic Information Systems, 17(3), 214-233. 12. Ellen, P. S., Bearden, W. O., & Sharma, S. (1991). Resistance to technological innovations: an examination of the role of self-efficacy and

performance satisfaction. Journal of the Academy of Marketing Science, 19(4), 297-307. 13. Han, S., & Yang, H. (2018). Understanding adoption of intelligent personal assistants: A parasocial relationship perspective. Industrial Management & Data Systems, 118(3), 618-636. 14. Hoy, M. B. (2018). Alexa, siri, cortana, and more: An introduction to voice assistants. Medical reference services quarterly, 37(1), 81-88. 15. https://mobiteam.de/en/the-rise-of-virtual-digital-assistant-usage-statistics-and-trends/ 16. https://www.statista.com/statistics/702926/united-states-digital-voice-assistants-survey-usage/ 17. Këpuska, V., & Bohouta, G. (2018, January). Next-generation of virtual personal assistants (Microsoft Cortana, Apple Siri, Amazon Alexa and Google Home). In 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC) (pp. 99-103). IEEE. 18. Kim, J., & Forsythe, S. (2008). Adoption of virtual try-on technology for online apparel shopping. Journal of Interactive Marketing, 22(2), 45-59. 19. Lamontagne, L., Laviolette, F., Khoury, R., & Bergeron-Guyard, A. (2014). A framework for building adaptive intelligent virtual assistants. In Proc. of the 13th IASTED Int’l Conf. on Artificial Intelligence and Applications (pp. 17-19). 20. Liang, H., Xue, Y., & Byrd, T. A. (2003). PDA usage in healthcare professionals: testing an extended technology acceptance model.



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International Journal of Mobile Communications, 1(4), 372-389. 21. Luna Cortés, G., & Royo Vela, M. (2013). The antecedents of consumers’ negative attitudes toward SMS advertising: a theoretical framework and empirical study. Journal of Interactive Advertising, 13(2), 109-117. 22. Martin, K., Borah, A. & Palmatier, R. (2017). Data Privacy: Effects on Customer and Firm Performance, Journal of Marketing, vol. 81, no. 1, pp.36-58 23. Moriuchi, E., & Takahashi, I. (2018). An empirical investigation of the factors motivating Japanese repeat consumers to review their shopping experiences. Journal of Business Research, 82, 381–390. 24. Niewiadomski, R., Demeure, V., & Pelachaud, C. (2010, September). Warmth, competence, believability and virtual agents. In International Conference on Intelligent Virtual Agents (pp. 272-285). Springer, Berlin, Heidelberg. 25. Nunnally, J.C. (1978). Psychometric theory. New York, NY: McGraw-Hill. 26. Oliveira, T., Thomas, M., Baptista, G. & Campos, F. (2016). Mobile payment: Understanding the determinants of customer adoption and intention to recommend the technology, Computers in Human Behavior, vol. 61, pp.404-414 27. Paul, F. (2015). How design and experience drive enterprise technology adoption. Network World.com, Available Online: http://www.networkworld.com/article/2880179/software/how-design-and-experiencedrive-enterprise-technology-adoption.html [Accessed 16 May 2017] 28. Pavlou, P., Liang, H. & Xue, Y. (2007). Understanding and mitigating

uncertainty in online exchange relationships: a principal-agent perspective, MIS Quarterly, vol. 31, no. 1, pp.105-136 29. Pikkarainen, T., Pikkarainen, K., Karjaluoto, H., & Pahnila, S. (2004). Consumer acceptance of online banking: an extension of the technology acceptance model. Internet research, 14(3), 224-235. 30. Purington, A., Taft, J. G., Sannon, S., Bazarova, N. N., & Taylor, S. H. (2017, May). Alexa is my new BFF: social roles, user satisfaction, and personification of the amazon echo. In Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems (pp. 2853-2859). ACM. 31. Rese, A., Baier, D., Geyer-Schulz, A., & Schreiber, S. (2017). How augmented reality apps are accepted by consumers: A comparative analysis using scales and opinions. Technological Forecasting and Social Change, 124, 306-319 32. Roca, J. C., Chiu, C. M., & Martínez, F. J. (2006). Understanding e-learning continuance intention: An extension of the technology acceptance model. International Journal of Human Computer Studies, 64(8), 683–696. 33. Sääksjärvi, M. & Morel, K. (2010). The development of a scale to measure consumer doubt toward new products, European Journal of Innovation Management, vol. 13, no. 3, pp.272-293 34. Silva, P. M., & Dias, G. A. (2007). Theories About Technology Acceptance: Why The Users Accept or Reject the Information Technology? Brazilian Journal of Information Science: Research Trends, 1(2).



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35. Solanki, M. R., & Dongaonkar, A. (2016). A Journey of human comfort: web 1.0 to web 4.0. International Journal of Research and Scientific Innovation (IJRSI), 3(IX), 75-78. 36. Thong, J., Hong, S., & Tam, K. (2006). The effects of post-adoption beliefs on the expectation-confirmation model for information technology continuance, International Journal of Human-Computer Studies, vol. 64, no. 9, pp.799-810 37. Venkatesh, V., & Davis, F. D. (1996). A model of the antecedents of perceived ease of use: Development and test. Decision sciences, 27(3), 451-481. 38. Venkatesh, V., Thong, J. & Xu, X. (2012). Consumer Acceptance and Use of Information Technology: Extending the unified theory of acceptance and use of

technology, MIS Quarterly, vol. 36, no. 1, pp.157-178 39. Verdegem, P. & De Marez, L. (2011). Rethinking determinants of ICT acceptance: Towards an integrated and comprehensive overview, Elsevier - Technovation, vol. 31, no. 8, pp.411-423 40. Xu, H., Dinev, T., Smith, H. J., & Hart, P. (2008). Examining the formation of individual's privacy concerns: Toward an integrative view. ICIS 2008 proceedings, 6. 41. Yaghoubi, N. M., & Bahmani, E. (2010). Factors affecting the adoption of online banking- an integration of technology acceptance model and theory of planned behavior. International Journal of Business and Management, 5(9), 159.

Table 1: Demographic Information of sample (n= 211)

Variables Frequencies Percentage Age (in years)

Less than 20 years 21-25 26-30 31-35

More than 36

14 118 30 8

41

6.67 55.8 14.3 3.7 19.5

Gender Male

Female

106 105

50.2 49.8

Education Graduate

Post Graduate PhD

67 139

5

31.8 65.9 2.4

VA usage Yes No

119 92

56.4 43.6



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Table 2: Purpose of usage as indicated by VA Users

Purpose of usage Frequencies (N=211) Percentage

Seeking Information

Entertainment

Passing time

Seeking Knowledge

Routine tasks such as booking, reminders etc

For companionship

For professional work

For monitoring purpose

97

85

30

48

62

8

34

14

80.2

70.2

24.8

39.7

51.2

6.6

28.1

11.6

Source: Authors

Table 3: Reasons for non-usage by Non-users (n=92)

Reasons for Non-Usage of VAs Frequencies (N=211) Percentage

Cost

Privacy Concerns

Technological hassles

Fear of misuse of the feature

Find the idea unappealing

Feel it’s a waste of time

Don’t see any use of it

Don’t have the application

VA require specific keywords which I don’t

Less knowledge

Preference to manual work

Lack of time

Don’t like dependency

31

107

57

56

15

25

37

23

1

1

1

1

1

14.5

50

27.6

26.2

7

11.7

17.3

10.7

0.5

0.5

0.5

0.5

0.5

Source: Authors

It was found that this top three barriers identified included Privacy concerns (50%), Technological hassles (27.6%) and Fear of misuse of the features (26.2%).

The reliability coefficients of the scales are presented in Table 4. All the scale reliabilities were above the prescribed value of 0.7 (Nunnally, 1978).



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Table 4: Reliability Analysis of Scales

Variable Number

of items

Cronbach’s

Alpha

Perceived Ease of Use

(PEU)

4 0.789

Perceived Usefulness 4 0.813

Attitude 4 0.732

Hedonic Motive 3 0.946

Privacy Concern 7 0.934

Perceived Intrusion 3 0.782

Intention to use 4 0.896

Table 5: Means scores of variables influencing Usage intention of VA



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Table 6: Independent samples t test results aimed to compare scores of users and non-

users

VARAIBLES F Sig. t df Sig. (2-tailed)Mean

DifferenceStd. Error Difference

Hypothesis

Lower Upper

PERCEIVED EASE OF USE 0.151 0.698 2.505 * 184 0.013 1.09689 0.4379 0.23294 1.96084 H1 Accepted PERCEIVED USEFULNESS 0.007 0.935 3.171 * 185 0.002 1.4949 0.47145 0.56479 2.42502 H2 Accepted ATTITUDE 2.538 0.113 2.199 185 0.029 0.75320 0.34246 0.07757 1.42883 H3 Accepted HEDONIC MOTIVE 1.647 0.201 1.996 * 193 0.047 0.7677 0.38456 0.00921 1.52618 H4 Accepted PERCEIVED INTRUSION 0.13 0.718 0.292 192 0.77 0.11017 0.37693 -0.63328 0.85363 H5 Rejected PRIVACY CONCERNS 0.208 0.649 -1.675 193 0.051 -1.58333 0.94511 -3.4474 0.28074 H6 Accepted INTENTION TO USE 1.189 0.277 5.917 193 0.0001 2.79698 0.47273 1.86459 3.72936 H7 Accepted

95% Confidence Interval of the

Difference

Documents

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