1 St. Xavier’s College (Autonomous), Kolkata, West Bengal, India
2 Department of Commerce (Evening), St. Xavier’s College (Autonomous), Kolkata, West Bengal, India
3 Department of Commerce (Morning), St. Xavier’s College (Autonomous), Kolkata, West Bengal, India
4 Deparment of Commerce and Management, St. Xavier’s University, Kolkata, West Bengal, India
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The astounding proliferation of mobile internet and technological innovations is one of the revelations of the 21st-century modern world. Electronic gadgets such as smartphones and tablets possess bewildering and alluring features which when blended with internet gives the modus vivendi of the consumers a completely different meaning. It has been observed that consumers have shown a strong penchant toward such technological vantages and innovative gadgets. In this context, the rise in the usage of music apps has been driven largely by the skyrocketing in the production and use of smartphones, coupled with the landscape of fast-paced internet. There are large number of consumers who have been adopting and using music apps, especially during the boring and monotonous period of COVID-19. The current research study is attempted at examining and analyzing the attitudes and behavior of the consumers toward the use of such music apps. For this purpose, a survey has been conducted in selected districts of West Bengal on the basis of “technology acceptance model”. The results reveal that consumers of West Bengal have positive perception toward music apps.
COVID-19 pandemic, mobile commerce, music apps, attitudes and behavior, technology acceptance model (TAM)
JEL Classification: M31, O33, O31
The growth and upsurge in the milieu of technological applications has been largely triggered by the dramatic escalation in the usage of internet, smartphones, and persistent percolation of technologies in the ambit of e-commerce. The eye-twitching technologies coupled with the consumers’ proclivity toward a more luxurious modus operandi has been a nifty driving force for the emergence of such alluring transitions in the landscape of consumers’ modus vivendi. In the past few years in a developing country like India, the spontaneous proliferation in the domain of e-commerce and mobile commerce has facilitated a plethora of hassle-free online applications such as mobile apps, food apps, shopping apps, taxi hailing apps, and so on that has been embraced by a huge proportion of Indian customers, thereby, bringing a change in their behavioral trajectories. The implementation of 4G network, low data cost, the juggernaut of technological innovations, the splendid metamorphosis in the features of latest smartphones, blended with penchant of Indian consumers in the quest for a more luxurious lifestyle has propelled the demand in the usage of various music apps such as Gaana, Wynk, JioSaavn, Spotify, Google Play Music, and so on. With the advent of music apps, a major clod of Indian consumers have been instantaneously willing to use such expedient services that are offered at free or minimal cost. The objective of current research endeavor is to investigate the intrinsic motivations, perceptions, and adoption mechanisms of users of music apps in the era of COVID-19.
Greater access to internet during COVID-19 has been an opportunistic tenet, increasing the consumers’ reliance of digital platforms. By capitalizing on the latest technological vantage points in the context of music apps, consumers are enjoying the benefits of high-quality audio streaming services at no cost. It can be safely asserted that consumers are done with the traditional modes of listening to music in the form of CDs, cassettes, and MP3 player which came with substantial cost and durability issues. The present age of digital revolution has created an impeccable aura which the consumers are quick to respond and usher into. Moreover, music apps serve the twin purpose of a low storage space and easy access to audio streaming which includes innumerable number and variety of songs which otherwise would have been impossible to store in a smartphone or tablet given the storage capacity of the devices, thus, proving to be a win-win situation. Thus, with such prolific amelioration in technology, customers are unshackled of the traditional stranglehold of music listening. According to the data published by Statista Research Department on April 23, 2021, there are five biggest players of music streaming app in India’s audio streaming market: Gaana, JioSaavn, Wynk, Spotify, and Google Play Music, followed by others. Gaana was perched with a whopping proportion of 30% in the country’s audio streaming market share, followed by JioSaavn which accounted for 24% while Wynk and Spotify reserved 15% each. Google Play was at the fourth place with 10% and the rest making up for the available 6%. Truth to be told, the Indian online audio streaming sector is said to be growing in leaps and bounds in the forthcoming years. However, music is unmonetized in India and thus, there lies a chink in the armor for all such music streaming companies to experiment with advertisement supported as well as paid streaming options (Srivastava, 2020). In 2020, Gaana showed that 85% of its users listened to Indian pop music while Spotify showed that even a bigger chunk of listeners stream international acts (Pavez, 2021).
One of the arduous tasks for any academic researcher exists in augmenting the current cognizant level of multiple factors which influences the adoption and usage of music apps, in light of “technology acceptance model” (TAM), where the crux objective is to probe into the motivations, preferences, attitudes, and behavioral intentions (BIs) of consumers toward such technology or apps. In the current research study, TAM has been modified to rejig the domains of “subjective norm” (SN) and “COVID-19 exigency.” Albeit few researches that have shown the blending of key components like “attitude–intention–behavior,” here in the present research study, the emphasis should be on conventional TAM with minor modifications.
Objectives of the Study
Review of Associated Literature
Research in the domain of technology and consumer behavior is not new, but probing into the perceptions of consumers toward music apps in the context of ‘new normal’ is a novel endeavor. Over the years, there have been massive pioneering and development of myriad theories and models for the purpose of explaining and prognosticating the behavior toward technologically driven acts. TAM is considered to be among the most influential models of technological acceptance, “an extension of the theory of reasoned action (TRA).”
Here, in the current research study, we are attempting to measure the attitudes and behavior of consumers in the state of West Bengal toward adoption and usage of music apps and hence it is almost imperative to discuss about TAM and use it for bolstering the worth of the study. “TAM is one of the most influential extensions of Theory of Reasoned Action (TRA),” which was brought into existence by Ajzen and Fishbein (1980). TAM is an amelioration over TRA since it was pillared on independent variables such as “perceived usefulness” (PU) and “perceived ease of use” (PEOU) as well as dependent variables like “attitude toward usage” (ATU).
Fred Davis first coined the term “perceived usefulness” which refers to the extent of a belief held by a person that the usage of a particular system would result in the enhancement of his/her job performance. Davis (1989) also defined the term “perceived ease of use” as the extent of a belief held by a person regarding the usage of a system which would be effortless. In further addition to the existing theory, Davis (1993) opined that the usage of the actual information system was a determination of a concept called “behavioral intention” which was a result of the combined force of ATU and PU. He defined it as “the subjective probability that an individual will perform a specified behavior.” ATU is a crux dependent variable in the TAM, and, in the words of Ajzen and Fishbein (2000), ATU is the evaluative effect of positive and negative emotions among individuals toward the usage of a particular system.
TAM is being continuously studied and expanded. Luam and Lin (2004) emphasized on the application of TAM, where their crux objective was the corroboration of the influence of mobile trust, PU and PEOU on services over wireless devices categorizing these crux components as independent variables having an inexorable impact on the customer adoption of SMS technology. It is noteworthy that the traditional model of TAM has also proven to be quite flexible to include independent constructs, most notably SN, as first introduced by Taylor and Todd (1995), who defined it as “the influence gained from social circle on whether or not to use a particular system.” The two crux upgrades include TAM2 (Venkatesh & Davis, 2000) and “unified theory of acceptance and use of technology” (Venkatesh et al., 2003). There has also been a proposition of TAM3 in the ambit of e-commerce, inclusive of trust effects and perceived risk on system usage (Venkatesh & Bala, 2008).
Roy (2017), in his study, incorporated a similar model blending the domain of SN with keywords of PU, PEOU, ATU and BI. The study focused on the consumer acceptance and adoption toward the use of taxi-hailing apps.
The motivation for developing a similar research model for the present study has been largely taken from Davis et al. (1989) and Roy (2017).
Any exigencies or unforeseen emergencies triggers abnormal behavior among consumers. The attitude and behavior of consumers also change in times of exigencies, and COVID-19 is no exception. The attitudinal display witnessed by the consumers during the time of this ongoing pandemic is quite astounding where consumers have become more cost conscious and alert toward health and safety. In this light, they are currently restricting their outdoor activities and trying to stay at home to the best possible extent, thus, adhering to the norms of COVID-19 protocols. However, the harsh isolations at home and traditional methods of music listening has become a burden on the consumers which they are looking forward to unshackle. As a result, consumers are seen adopting and using music apps and listening to variety of songs, even international songs, after a comfy and hassle-free access. This phenomenon is also having a prodigious impact on the attitudes and behavior of the consumers.
Albeit, past researches that have been conducted taking into consideration the concept of SN in the context of TAM are very rare. In addition to this, we find it extremely arduous to come across the domain of COVID-19 exigency used as a construct in TAM, by further introspection of existent literature. This in fact is the essence of our present research study.
An intricate analysis of further literature would reveal that there are hardly any research papers that emphasize to measure the perception of consumers toward the adoption and usage of music apps. Past few researches have rather focused on consumer behavior toward online music services, and, as a result, there lies an extensive domain of unexplored research that could be undertaken to examine and analyze the consumer preferences and attitudes toward music apps.
The existent literature in the domain of consumer behavior and TAM is vast. But a major innovation in contributing to the existing associated literature would be to blend the keywords of SN and “exigency” (COVID-19). The present research study is a novel effort, where the grueling task involved has been blending such key constructs to unfurl the underlying motivations, attitudes, BIs, and perception of consumers, which is the very essence of our purported endeavor.
The current research study is an endeavor, attempted in developing a conceptual framework of the user adoption and usage of music apps, pillared on marginally modified TAM. The below model is a re-modified TAM. The constructs namely SN and “exigencies” (COVID-19)” have been incorporated to cater to the influence of peer groups and urgent unforeseen needs, respectively. Therefore, our research model comprises of 6 constructs, which have been developed and presented below. For this purpose, the following hypotheses have been developed and substantiated through the research model represented above.
Figure 1. Research Model
Source: The authors.
H1: PU has a positive influence on ATU.
H2: PU has a positive influence on BI of consumers.
H3: PEOU has a positive influence on PU.
H4: PEOU has a positive influence on ATU.
H5: SN has a positive influence on BI of consumers.
H6: Exigency (COVID-19) has a positive influence on BI of consumers.
H7: ATU has a positive influence on BI of consumers.
Data and Methodology
A rigorous research was conducted including both primary and secondary data. Secondary data was used to create a robust foundation of conceptual framework of the present research study. For this purpose, several research papers have been acquired from various authentic and reliable e-resource websites such as SAGE, JSTOR, BASE, Google Scholar, and so on. The process of primary data collection includes a survey which has been conducted on a total sample size of 500 respondents in different districts of West Bengal such as Kolkata, Howrah, Hooghly, Burdwan, and Birbhum, belonging to the age group of 18–55 years, engaged in various occupations, all of them using music apps. To this end, a close-ended questionnaire was developed. Most of the questionnaires were mailed while others were randomly doled out to the respondents. The questions in the questionnaire were mostly self-developed albeit few questions have been adopted from previous researches. The questionnaire contained 23 questions under 6 segments, namely, PU, PEOU, SN, “exigencies” (COVID-19), ATU, and BI. A five-point Likert scale (5 = strongly agree and 1 = strongly disagree) has been used to measure the concepts. There were few responses that were erroneous and some were not returned, hence, those responses had to be rejected. After the rejection of such responses, the final valid responses stood at 462.
As observed in Table 1, the number of male respondents (n = 244) exceeds the female respondents (n = 218). Most of the respondents belong to a young age group in the category of 26–35 years and 36–45 years. Majority of the respondents are either students or servicemen. We also see that most of the respondents having a moderately strong level of income perched in the range of 25.1K–50K.
Table 1. Representation of Descriptive Statistics
Source: The authors.
For the purpose of testing whether the variables are consistent or not a Cronbach’s Alpha has been run which would help to examine the scale reliability of the constructs. All the “Cronbach’s Alpha” values as evidenced in the above table exceed the ideal value of 0.7, while “Correlations of Corrected Items” are all above the ideal value of 0.5 (see Table 2).
Table 2. Reliability Statistics
Source: The authors.
Convergent and Divergent Validity Test
A convergent validity test has been conducted to check for the convergence of items. According to Fornell and Larcker (1981), “The convergent validity of scale items are determined by their respective factor loadings, composite reliability and average variance extracted.” The loadings of “confirmatory factor analysis” (CFA) and composite reliabilities of all factors report figures of above required level of 0.7, while average variance explained (AVE) report figures above required level of 0.5 (see Table 3).
Table 3. Convergent Validity Results
Source: The authors.
The usage of “square root of ACE” and the “correlation coefficient matrix” is important for testing the divergent validity of constructs. As per Fornell and Larcker (1981), “Discriminant validity was obtained by comparing the shared variance between factors with the AVE from the individual factors.” The below matrix shows that maximum shared variance and average shared variance between factors are less compared to AVE and also the square root of AVE is higher compared to the correlations of inter-constructs, hence, satisfying the discriminant validity test (see Table 4).
Table 4. Divergent Validity Results
Source: The authors.
Test for Structural Equation Modelling
Structural equation modelling (SEM) has been performed to delve into the relationships existing between 6 variables, namely, PEU, PU, ATU, SN, BI, and EXC. The rationality exist in testing the fit between the model and the obtained data. The first stage of making inference about the results of SEM encompasses a review of “fit indices.” All the fit indices when juxtaposed with their corresponded values which has been suggested will give a good model fit “ratio of chi-square to its degrees of freedom” (χ2/df) = 1.977, “goodness-of-fit index” (GFI) = 0.958, “Adjusted GFI” (AGFI) = 0.936, “relative fit index” (RFI) = 0.967, “comparative fit index” (CFI) = 0.983 and “Root Mean Square Error of Approximation” (RMSEA) = 0.042 (see Table 5).
Table 5. Indices for Measure of “Goodness-of-Fit”
Source: The authors.
To this end, the results of hypothesis testing have been obtained. The Table 6 clearly represents the validation of all the hypotheses through the path analysis. It can be concluded that PU and PEOU favorably impact attitudes toward usage with each reporting figures of (β = 0.192, p < .05) and (β = 0.402, p < .05), respectively, thereby supporting H1 and H4. The same could be safely asserted for the relationship between PU, SN, ATU, and exigency (COVID-19), which are all significantly associated with BI of consumers toward music apps, with each reporting figures of β = 0.198, p < .05; β = 0.405, p < .05; β = 0.443, p < .05; and β = 0.402, p < .05 respectively substantiating H2, H5, H6 and H7. The relationship between PEOU and PU is also linked with figure reporting (β = 0.609, p < .05), thus, substantiating H2.
Table 6. Results of Path Validation (Hypothesis Testing)
Source: The authors.
Deliberation of Research Findings
The present research study was a pioneering effort in delving into the consumer attitude and behavior toward music apps by applying TAM. On the basis of our proposed research model, we explored the liaisons among the traditional TAM components and contemporaneous components such as SN and exigency (COVID-19). The findings of the data analysis of the survey have been examined and proved to have positive and significant relationships with each other as per the hypothetical statements. The current research study is highly relevant in the context of recent proliferation in the milieu of technological innovations and advancements, resulting in the upsurge of e-commerce, m-commerce, smartphones, online audio streaming services, and music apps. According to the first hypothesis, PU had a positive relationship with ATU as the extent to which the importance of usefulness will be psychologically casted would have a bearing on the attitude of the consumers. As per the second hypothesis, it was also observed that PU was related to BI. An explanation for this would be that the consumers are always in the quest for using a beneficial application which would make their lives more convenient. We evidence, in the third hypothesis testing, that PU was strongly influenced by PEOU. This hints that providing appropriate user training is essential for bolstering the consumers’ perception of the usefulness of a technology. Besides, PEOU was also positively related to ATU, validating our fourth hypothesis. The fifth hypothesis was attempted at examining the relationship between SN and BIs. The impact cast by social circle had a significant impact on the BI of consumers toward adoption and usage of music apps. This is a crucial finding as local clubs, colleagues, or opinion leaders shape the perceptions and attitudes of people toward a technology. As observed by the sixth hypothesis, we have comprehended that the attitude of consumers toward music apps has been instrumental in shaping the BI of such consumers, as both mental as well as physical faculties are a nifty driving force in developing the perceived likelihood of consumers. Finally, the seventh hypothesis analyzed the relationship between exigency (COVID-19) and BIs. Any unforeseen exigencies would trigger an abnormal behavior among people. An example would be any war which can bring a stunning change in the consumption trajectories of the consumers as they would hoard food grains, spend less on luxury items, cut costs, focus on health and safety, and so on. The current situation of this ongoing COVID-19 pandemic is also similar, where people are relinquishing old ways of doing things for the ravishing new ones. The adoption and usage of music apps during COVID-19 has also been easy and convenient largely due to the skyrocketing of internet usage and penetration. Such prolonged attitudes observed among the consumers during this “new normal” has brought a bewildering change in their behavior as well.
The breathtaking metamorphosis in the landscape of technology has triggered a bewildering growth in e-commerce and m-commerce. The smartphones and tablets of today exhibit qualities like never, hence, making the modus operandi of people rather luxurious. The modern technologies have pulled off a stunning heist by intriguing the customers’ frame of mind. Taking into consideration the proclivity of consumers in the contemporaneous panorama, these amelioration in the circa of technology, particularly e-commerce and m-commerce, was a much-needed tonic. The current research study highlighted certain crux elements under TAM constructs which shall be highly relevant in guiding future researches. People in West Bengal prodigiously valuing the services of online audio streaming especially during these times of pandemic is a manifestation in the sudden escalation in the adoption and usage of music apps and other sources of online audio streaming. Truth to be told, the framework of TAM has been criticized because of its debatable heuristic value, constrained explanatory, and predictive power and triviality. Despite such shortcomings, it is by far, the most popular theoretical model used in academia. The present research study is robust enough to provide valuable conclusions concerning consumer behavior displayed toward adoption and usage of music apps.
Declaration of Conflicting Interests
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
The authors received no financial support for the research, authorship, and/or publication of this article.
Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behaviour. Prentice Hall.
Ajzen, I., & Fishbein, M. (2000). Attitudes and the attitude-behavior relation: Reasoned and automatic processes. European Review of Social Psychology, 11(1), 1–33. https://doi.org/10.1080/14792779943000116
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of Information Technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
Davis, F. D. (1993). User acceptance of information technology: System characteristics, user perceptions and behavioural impacts. International Journal of Man-Machine Studies, 38(3), 475–487. https://doi.org/10.1006/imms.1993.1022
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
Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of Marketing Research, 18(3), 382–388. https://doi.org/10.1177/002224378101800313
Luam, P., & Lin, H. H. (2004). Towards an understanding of the behavioral intention to use mobile banking. Computers in Human Behavior, 21(6), 340–348.
Pavez, T. (2021). Music streaming trends and music services in India: An overview. SonoSuite. http://www.sonosuite.com/blog/music-streaming-trends-in-india/
Roy, S. (2017). Scrutinizing the factors influencing customer adoption of app-based cab services: An application of the technology acceptance model. IUP Journal of Marketing Management, 16(4), 54–69.
Srivastava, A. (2020). Market share of music streaming apps in India. AS. http://www.arpitsrivastava.com/market-share-of-music-streaming-apps-in-india/
Taylor, S., & Todd, P. A. (1995). Understanding Information Technology usage: A test of competing models. Information Systems Research, 6(2), 144–176. https://doi.org/10.1287/isre.6.2.144
Venkatesh, V., & Bala, H. (2008). Technology acceptance Model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315.
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.184.108.40.20626
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of Information Technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540