Keywords

Online Music, E-commerce, Personality Traits, Online Shopping.

Introduction

Increasing smart phone devices, low data cost and internet penetration has made Indian music streaming market among the next biggest segment of e-commerce industry. “Streaming has been around for a long time but it was constrained by consumers thinking very hard about how much it was going to cost them, because data charges were so high. In the last two years or so, data prices have dropped almost 90%, so the industry is really coming into its own” Jehil Thakkar, partner at Deloitte India, said. The online music started gaining in 2006-07 in India when music companies like t-series, Saregama, HMV and Sony begin publishing their tracks on online platforms. The idea was to remove the piracy and reproduction of tracks from retail market, and felt the presence in the minds of youth while entering into their digital devices. With its primarily young, rapidly growing population, 1.3 billion listeners and many music professionals are now looking music industry of India to become the next great frontier of e-commerce segment. Country’s growing online population fosters the revenues of Indian music industry. However, India’s massive film industry still plays an outsized role in the music business with 80% of the country’s music revenue reportedly generated by soundtracks for Bollywood films.

In 2018, India became the 5th biggest economy in the world in terms of the current GDP. It’s the fastest-growing economy out of the top-10, projected 7% CAGR up until 2023. This almost unprecedented economic growth is powered by the population of 1.35 billion people, which keeps on rising-India, is expected to overtake China in total residents and become the world’s most populated country by 2027 (Ahlgren, 2000 & Krasilovsky & Shemel, 2000).

Literature Review

Research on technology acceptance is as old as its inception in India. First website was launched in 1991 and in the year 1993 Davis proposed Technology Acceptance Model, which initially has been used to explain and predict computer acceptance. TAM proposed two new beliefs – perceived usefulness (PU) and perceived ease of use (PEOU) – and ignores the influence of subjective norm. Later on various factors as Transaction Cost/Price/Charges, Convenience, Trust, Privacy, Social Pressure, degree of customization etc were added.

Research related to media consumption was initiated by Graham et al. (2002) he revealed that media consumption is motivated by a set of cognitive and affective needs of audience, while uses and gratifications paradigm conceptualizes the audience member as goal-oriented individuals. Once individual felt need to consume product, search for available alternatives and select the one that is convenient and trust worthy. Balasubraman et al. (2002) stated that convenience and trust of using m-commerce application at any time builds positive perception in the minds of users, which ultimately allow them to continue the use of m-commerce applications.

Music listeners adopted online platforms due to cultural and economic pressures from their peer groups. Throsby (2002) examined the role of music both as a primary form of cultural expression and as a powerful economic force in a globalizing world. He pointed out the role of music in the context of economic development, as one of the most important creative industries offering prospects of both cultural and economic benefits in the developing world. Lin et al. (2014) conducted research to determine the uses and gratification for mobile phone applications, the findings suggest that social benefits, immediate access & mobility, entertainment, self-status seeking, pursuit of happiness, information seeking, and socializing are the primary factors driving app user’s adopting behavior.

Cost is the major concerned for adapting any product in online shopping environment. Music producers had adopted online platform to reduce the production and distribution cost and passed the benefits directly to the listeners. Lin (2006) found in her study as expected, the radio listeners who were willing to pay a fee to download music from an online source were also willing to pay a fee to subscribe to a digital satellite radio service. The study thus further confirms past work on the construct of technology clusters, which theorizes that audiences tend to adopt functionally similar new technologies in a cluster to help reconfigure their home media environment. This type of adoption process could involve the functional objective of either supplementing or displacing an old technology (i.e., the terrestrial radio), or complementing a functionally similar new technology (i.e., online-music download service). According to Wiafe (2012), majority (51%) of the respondents indicated that the reason for the adoption of e-commerce in the music industry was primarily to reduce transaction cost in marketing and sales of digital music. Study conducted by Lee et al. (2018) show results that there is a significant relationship between online streaming, cost and cable TV. Moreover, no statistical relationship found between cost and online streaming. Customer service is the main driver to customer satisfaction while social trends persuade the adoption of online streaming. Apart from music other industries using online platform received acceptance due to cost.

Categories of music are wide; every listener has its own choice of sound tracks. Hence music streaming provides choice to customize play list. Thus previous research in field of customization as: On post factors affecting post-adoption in a music streaming application for young adults. The research reveals the ubiquity and personalization affect the usefulness of e music in Indonesia. Kamehkhosh et al. (2020) researched the effects of recommendations on the playlist creation behavior of users; study reveals that the mere presence of the recommendations impacts the choices of the participants, even in cases when none of the recommendations was actually chosen.

Previous research studies focuses on various motives of adopting e-music as: personal, product customization, cost, trust, perceived utility, perceived ease of use, privacy etc. Attitudes toward online shopping and intention to shop online are not only affected by ease of use, usefulness, and enjoyment, but also by exogenous factors like consumer traits, situational factors, product characteristics, previous online shopping experiences, and trust in online shopping. It has been observed that perception, attitude, beliefs and personality trait guide the behavior of individual. Among all the psychological characteristics personality traits are considered to be somehow stable. Hence this article uses personality trait of listener to investigate the relationship with motive of adopting e-music in India.

Objectives of Study

Every customer follows decision making process is systematic manner and before buying any product every individual search for alternatives, analyze and ultimately take decision where to buy or not to buy. Online channels are also analyzed and decided whether to adapt it or not. Each and every customer may not adapt online channel to acquire all the products they consume. Some may uses online travel, e-shopping, online education but may not use online banking or e-music. Not what all products to be shopped from online channel and others, depends on the motivating factors. Every customer possess certain dominating personality traits that govern their behavior, which ultimately influence the decision making process. This study focuses to:

•Analyze the dominating personality traits of e-music listeners.

• Determine the motivating factors of e-music listeners.

• Analyze the impact of personality trait on motives of e-music listeners.

Research Methodology

A questionnaire was developed to collect the responses from e-music listener. It is considered to be the most effective method for collecting primary data. Both online and offline questionnaires were being used to collect the data. The convenient sample of 298 respondents was considered to analyze the responses. Respondents were contacted through mailers and direct interaction. The questionnaire had structured questions of two sets, i.e. Personality trait of e-music listeners (set 1) and Motives of e-music listeners (set 2). After applying the reliability test 274 responses were found to be suitable for further analysis. To analyze the dominating personality traits BFI score is calculated by adding the answers to a series of structured questions.

Results & Discussion

Data collected from the 298 respondents, was validated and reliability tests were applied to derive the result for further analysis of the research study. The 274 responses were found to be suitable for further analysis and to interpret results principal factor analysis was applied to analyze the motives to adopt e-music.

H_0a1 There is no relationship between individuals using internet and e-music listening

Observations

Respondents were instructed to rate the use of Internet on 5 point likert scale and rate how often they use e-music. Preferences of respondents were analyzed using Chi-Square test as mentioned below (Table 1 & Table 2).

Results show that the Pearson chi-square value 0.016 is less than α =0.080, which ultimately rejects the null hypothesis. Hence a positive relationship exists between individuals using the internet for shopping and online apparel buying.

Hypotheses (Personality Trait)

Personality trait e-music listeners of India.

H_0b1 Individuals using e-music does not have Openness to Experience as dominant personality character.

H_0b2 Individuals using e-music does not have Conscientiousness as dominant personality character.

H_0b3 Individuals using e-music does not have Extraversion as dominant personality character.

H_0b4 Individuals using e-music does not have Agreeableness as dominant personality character.

H_0b5 Individuals using e-music does not have Neuroticism as dominant personality character.

After analyzing the frequencies of 274 respondents, it was observed that 147 respondents were having a higher BFI Index of Extraversion and 73 respondents were having high BFI Index of Agreeableness as mentioned in Table 3. Further analysis of responses revealed that 53% were having Extraversion and 27% were having Agreeableness as dominating personality traits.

Results interpreted after analyzing the data collected from respondents using e-music, it could be ascertained that hypotheses as mentioned in Table 4, H0b2 and H0b3 were rejected, having higher β coefficients and T values at 95% significance level. It could be stated that individuals using e-music in India possesses extraversion and conscientiousnessas dominating personality traits.

Hypotheses (Motives of Individuals Having Extraversion as Dominant Characteristics of Personality Trait)

Individuals having Extraversion as dominating personality traits are predicted as Energetic, Assertiveness, Sociability, and the tendency to seek stimulation in the company of others, and talkativeness. Attention seeking and dominating personality are presumed from individuals possessing high extraversion whereas individuals having low extraversion a reserved, reflective personality, which can be perceived as aloof or self-absorbed. Extroverted people may appear more dominant in social settings, as opposed to introverted people in his setting. The study aims to identify the relationship between individuals having dominant personality trait Extraversion and motives of using e-music in India.

H_0c1 Individuals having Extraversion as dominating personality traits are not motivated by Usefulness of e-music.

H_0c2 Individuals having Extraversion as dominating personality traits are not motivated by Ease to use of e-music.

H_0c3 Individuals having Extraversion as dominating personality traits are not motivated by Convenience of e-music.

H_0c4 Individuals having Extraversion as dominating personality traits are not motivated by Perceived Control of e-music.

H_0c5 Individuals having Extraversion as dominating personality traits are not motivated by Privacy of e-music.

H_0c6 Individuals having Extraversion as dominating personality traits are not motivated by Trust of e-music.

The analysis of e-music users for ascertaining the motives for adopting online channel was evaluated on the KMO sampling measure. All the motives qualify for further analysis i.e. extraction loading, whereas online Price/Charge/Cost Savings, and Privacy were having factors >60% (Table 5).

Price

Online platform provide better purchase conditions and lower price as compared to stores. The expectation of consumers to acquire products at lower price to increase satisfaction strengthens the acceptance of online platform. Moreover discounts while purchasing influence consumers to believe in prices, and ultimately they affect their satisfaction. E-music users tendency to adopt online channel was analyzed on 8 motivating factors and were reduced by using principal component analysis (Table 6).

The analysis of e-music users having dominating personality trait of extraversion were reduced to transaction charges with beta of 0.850. Hence principal component analysis verifies the effect of transaction charges on motives of adopting e-music channel.

Privacy

The collection of personal information, control over theuse of personal data, and awareness of privacy practices and how personal information is used were the dimensions interpreted as influential in online business (Malhotra et al., 2004). The analysis of privacy dimension was made on the basis of statements as browsing, confidence, transaction, security, and personal details.

The data collected from 147 participants using e-music has shown concern related to privacy of online channel. Principal Component Analysis reduces the 5 factors of privacy to one i.e. personal detail with beta of 0.886 (Table 7).

Proposed Model

The rapid development of the Internet and its effect on daily life has introduced a new consumer profile which is referred to as the “online consumer”. Such consumers are influenced by various factors and they have different purchasing habits concerning traditional consumers. After analyzing the results of the reliability test, 5 out of 6 motivating factors were identified as relevant for further analysis of model fit (Figure 1 & Table 8).

Figure 1 Proposed Model for Analyzing the Impact of Personality Traits on Motives of Adopting E-Music in India

The 7 motivating factors influencing e-music listeners to adopt online channel were tested on confirmatory factor analysis (Table 9). Results were measured on 3 different dimensions goodness of model fit, badness of model fit and validity of model using AMOS 21 (trial version). The proposed model validated by CMIN/DF value 3.169 near to 3.0 (standard) and CFI value 0.929 (accept CFI >0.90) as mentioned in Table 10. Model tends to be error free and being confirmed by badness of fit with RMSEA value 0.089 (standard <0.10) as mentioned in Table 11. Finally proposed model validated by CR=0.85 (standard CR>0.7), AVE=0.78 (standard AVE>0.5) and CR >AVE Calculate Values 0.85 > 0.78.

The results of study reveal that 6 out of 7 motives proposed in model validate that motives influence the adoption of e-music in India (Table 12). Further all the objectives established were verified by the data collected from 274 e-music listener in the study. The study reveals that an e-music listener in India does possess either Conscientiousness or Extraversion as dominating personality trait. They are motivated by easiness to search, knowledge of platform, validity of required file, navigation/search-ability of application, effortless transmission and cost effective transactions. Most importantly data was able to establish and validate the relationship between motives and traits to adopt e-music in India. This can be attributed to the fact that this is a recently emerged research area. The originality of our paper stems from highlighting a future research agenda for consumers' online purchase behavior.

Conclusion

Hence it is concluded from a study of analysing the impact of personality traits on adopting e-music in India that, individuals using online channels possess dominating personality traits either Conscientiousness or Extraversion. It was revealed that those individuals who scored high on Conscientiousness are motivated by Perceived Usefulness, Perceived Ease of Use/Convenience, Enjoyment/Adventure/Fun, Information Availability, Price/Charge/Cost Savings from online platform. Whereas, the score high on Extraversion shows motivation by Price/Charge/Cost Savings and Privacy of channel. Finally, it could be ascertained from research that the personality traits of consumers influence the motives of adopting an e-music in India.

Direction for Future Research

The study focused on analysing the impact of personality traits on motives of adopting e-music in India. The researcher proposes to carry similar study in a future to investigate other demographic variables – Age, Gender, Occupation, Income, Education, etc. E-commerce industry is wide enough to predict and categorize on the basis of limited data, hence researcher proposes to continue exploring other category of products available on online shopping channel. Intention, attitude, belief etc are the other variables of decision making process that needs to be given attention to strengthen the proposed model. Moreover, it is proposed to conduct this study at various locations to generalize the conclusion.

References

1. Ahlgren, B. (2000). Electronic trade in music: The shift from physical to non-physical products. Unpublished doctoral dissertation, Department of Media Technology and Graphic Arts, Royal Institute of Technology (KTH), Stockholm, Sweden.
2. Balasubraman, S., Peterson, R.A., & Jarvenpaa, S.L. (2002). Exploring the implications of m-commerce for markets and marketing. Journal of the Academy of Marketing Science, 30(4), 348-361.
3. Throsby, D. (2002). The music industry in the new millennium: Global and local perspectives. The Global Alliance for Cultural Diversity. In Paris, Division of Arts and Cultural. Paris.
4. Graham, G., Burnes, B., & Hardaker, G. (2002). The impact of the internet on the structure and conduct of the music market supply chain.
5. Kamehkhosh, I., Bonnin, G., & Jannach, D. (2020). Effects of recommendations on the playlist creation behavior of users. User Modeling and User-Adapted Interaction, 30(2), 285-322.
6. Krasilovsky, M.W., & Shemel, S. (2000). This business of music: The definitive guide to the music industry. New York: Billboard Books.
7. Lee, C.C., Nagpal, P., Ruane, S.G., & Lim, H.S. (2018). Factors affecting online streaming subscriptions. Communications of the IIMA, 16(1), 2.
8. Lin, C.A. (2006). Predicting satellite radio adoption via listening motives, activity, and format preference. Journal of Broadcasting & Electronic Media, 50(1), 140-159.
9. Lin, Y.H., Fang, C.H., & Hsu, C.L. (2014). Determining uses and gratifications for mobile phone apps. In Future information technology. Springer, Berlin, Heidelberg.