0% READ
FULL TEXT
INTRODUCTION
The robust digital transformation of healthcare ecosystem in India has redefined the way consumers ingress pharmaceutical and medical platforms, especially via the initiation of e-pharmacy applications, which is now becoming an easy alternative of brick-and-mortar pharmacies (EY India, 2020), gaining attention because of their value-added services like door step delivery of medicines, price translucency, digital consultations, and better continuity of care. The COVID-19 pandemic also played a major role in accelerating its adoption, because of it intensified need of contactless healthcare delivery infrastructure.
In spite of its emerging relevance, e-pharmacy adoption in Kolkata, West Bengal remains questionable (Ltd., n.d), indulged by several behavioural, mechanical and trust-worthiness constructs. Former studies using prior frameworks of the Technology Acceptance Model (TAM) (Davis, 1989) and the Unified Theory of Acceptance and Use of Technology (UTAUT/UTAUT2) (Venkatesh et al., 2012) have cultured the importance of determinants like performance expectancy, effort expectancy and social influence in analysing technology adoption. Though, current empiric findings reveal mixed outcome. To be precise, it has been noticed that few Indian studies indicate that performance expectancy do not always have an immediate sway on intention of e-pharmacy adoption (Ahalawat et al., 2024), though others authenticate that perceived ease of use and efficiency prominently instigate consumers to accept online pharmacy services (Ezeudoka and Fan, 2024; Srivastava and Raina, 2020). Moving on to the next determinant, it can be said that the amount of comfort a consumer enjoys while using a technology is Effort expectancy (Chauhan and Jaiswal, 2016). Likewise, certain studies also confirm a constructive effect of effort expectancy on the intention of its adoption (Rafique et al., 2023), though across specific demographic and facilitating settings, others have found non-significant relationships (Yin et al., 2016; AlSideiri et al., 2025). Trust, too, has appeared as an unavoidable moderator in e- healthcare adoption, especially mentioned concerns regarding counterfeit medicines, data falsification and digital monetary fraud (Alsadoun et al., 2022). A significant positive influence of trust has also been noticed on consumers’ intention to shop digitally (Rehman et al., 2019), in case of getting online medicines also, but trust as a moderating factor is not yet cultured much where several research including Singh and Kumar,2024 has revealed its ability in turning consumer’s actual behavioural pattern in purchasing medicines digitally.
By 2000, a reliable body of research on consumer behaviour to adoption of technology has seen to be emerging out (Venkatesh and Davis, 2000), which in result constricted the demand of these mentioned theories to some extents, but among all these, many factors that has seen to be affecting the adoption of e-pharmacy is performance expectancy (Singh and Kumar, 2024), effort expectancy (Srivastava and Raina, 2020) and trust (Fan et al., 2024).
The triumph of any digital innovation needs consumers’ adoption, which is determined by technical and personalised characteristics and attributes (Otto and Harst, 2019). To perform a specific behaviour, an individual’s perceived interest or willingness can be defined as behavioural intention (Brezavšček et al., 2016). User behaviour again indicates the measures, predilections and interactivities of consumers while experiencing a service, product or any kind of technology (Pedersen, 2024). Meanwhile some research states that behavioural intention strongly influences the real-life utilization of electronic health services (Krishnan and Rahim, 2021), while Dwivedi et al., 2020 estimated that conditional and cultural challenges may nullify this particular relationship. The belief of consumers, both in voluntary and involuntary contexts, is widely regarded as one of the strongest predictors of behavioral intentions (Venkatesh et al., 2016). Building upon this notion, the primary objective of this study is to evaluate whether consumers’ behavioral intentions significantly predict their actual use of e-pharmacy services in Kolkata, West Bengal. By examining this relationship, the study aims to assist policymakers in developing strategies to promote e-pharmacy adoption. The secondary objective is to understand whether the factors derived through rigorous literature review, like, performance expectancy, effort expectancy and trust is influencing the behavioural intention of adopting e-pharmacy among the consumers, from which we can come to:
- 1H0: Performance Expectancy is not having significant positive linear relationship with Behavioural Intention
- 1HA: Performance Expectancy is having significant positive linear relationship with Behavioural Intention in adopting E-pharmacy
- 2H0: Effort Expectancy is not having significant positive linear relationship with Behavioural Intention in adopting E-pharmacy
- 2HA: Effort Expectancy is having significant positive linear relationship with Behavioural Intention in adopting E-pharmacy
- 3H0: Trust is not having significant positive linear relationship with Behavioural Intention in adopting E-pharmacy 3HA: Trust is having significant positive linear relationship with Behavioural Intention in adopting E-pharmacy
- 4H0: Behavioural Intention is having not significant positive linear relationship with Use Behaviour in adopting E-pharmacy
- 4HA: Behavioural Intention is having significant positive linear relationship with Use Behaviour in adopting E-pharmacy.
The proposed model of the study is depicted in Figure 1.
MATERIALS AND METHODS
This quantitative and cross-sectional research study is conducted to examine the constructs driving behavioural intention, ultimately leading to use behaviour towards e-pharmacy adoption in Kolkata. Primary data was collected using a structured close-ended questionnaire (Vidal-Silva et al., 2024; Singh and Kumar, 2024; Venkatesh et al., 2012) by distributing to the adult consumers staying in urban and semi-urban areas of Kolkata, who has at least used e-pharmacy once administering though Google Forms, keeping the privacy of the respondents. The factors used in the questionnaire rated on a 5-point Likert scale are Performance Expectancy, Effort Expectancy, Trust, Behavioural Intention and Use Behaviour. The sampling technique used here was a non-probabilistic sampling method, mostly a combination of purposive sampling and snowball sampling. The inclusion of purposive sampling was necessary to make sure the involvement of respondents who had former experience of using e-pharmacy, further, the necessity of snowball sampling was there as the study need to reach a vast network of consumers of the online pharmacy applications, so that unidentified consumers can be accessed easily. A total of 235 response was collected, among which, excluding all the missing responses and the wrong data, 204 valid responses were selected to proceed further, which according to the Regression Rule of Thumb (Green, 1991), is sufficient. The data were analysed through SPSS, 21, which includes multiple linear regression to evaluate the relationships among the determinants. Model fit was detected using Proportion of Variation Test (R²), Adjusted R², Durbin-Watson statistics, t-values, significance levels and VIF scores identify multicollinearity.
Table 1 represent the model summary table, using a multiple linear regression model where we have three independent variables namely, performance expectancy, effort expectancy and trust with one dependent variable, behavioural intention. It is stated that the value of R2 is 0.924, indicating that there is a very strong positive relationship between the independent and the dependant variables, which is also an indication of excellent model fit. With that, as the value of Durbin-Watson test is 2.29, it also states that there is no serious violation of regression assumptions.
| Model | R | R Square | Adjusted R Square | Std. Error of the Estimate | Change Statistics | Durbin-Watson | ||||
|---|---|---|---|---|---|---|---|---|---|---|
| R Square Change | F Change | df1 | df 2 | Sig. F Change | ||||||
| 1 | 0.961a | 0.924 | 0.92 | 0.19193 | 0.924 | 215.3 | 5 | 199 | <0.001 | 2.29 |
Further Table 2 simplifies the relationship of performance expectancy, effort expectancy and trust with the behavioural intention of adopting e - pharmacy among the population. It clearly conditions that the independent variables (PE, EE and T) are related with the dependent variable (BI) in the form of
| Model | Unstandardized Coefficient | Standardized Coefficient | t | Sig. | 95% Confidence Interval for B | Collinearity Statistics | |||
|---|---|---|---|---|---|---|---|---|---|
| B | Std. Error | Beta | Lower Bound | Upper Bound | Tolerance | VIF | |||
| Constant | -0.168 | 0.154 | -1.094 | 0.279 | -0.477 | 0.14 | |||
| PE | 0.322 | 0.09 | 0.31 | 3.593 | 0 | 0.142 | 0.501 | 0.192 | 5.219 |
| EE | 0.187 | 0.095 | 0.193 | 1.974 | 0.05 | -0.003 | 0.377 | 0.15 | 6.679 |
| T | 0.527 | 0.067 | 0.521 | 7.844 | 0 | 0.392 | 0.662 | 0.324 | 3.084 |
As the Beta value of Performance Expectancy (PE), Effort Expectancy (EE) and Trust (T) is 0.322, 0.187 and 0.527 respectively, the standardized coefficients are ranging from strongest to moderate significance. As the VIF value of Trust is 3.084, it is the safest among the three, indicating multi collinearity. As the Sig. value is approximately less than 0.05 in all the three cases, we can conclude that we can reject the first three null hypothesis (1H0, 2H0 and 3H0), and accept all the alternatives stating combining that, Performance Expectancy, Effort Expectancy and Trust are having significant positive linear relationship with Behavioural Intention in adopting E-pharmacy.
To understand the relationship of behavioural intention with the use behaviour, Table 3 points out the model summary for this relationship, through a multiple linear regression model where in this case the only independent variables namely, behavioural intention with the dependent variable, use behaviour. In this scenario, R2 value is 0.935, also stating a very strong positive relationship between the independent and the dependant variables, labelling it as an excellent model fit. The value of Durbin-Watson test is 1.889, which also indicates no serious violation of regression assumptions in this case too.
| Model | R | R Square | Adjusted R Square | Std. Error of the Estimate | Change Statistics | Durbin-Watson | ||||
|---|---|---|---|---|---|---|---|---|---|---|
| R Square Change | F Change | df1 | df 2 | Sig. F Change | ||||||
| 1 | 0.967a | 0.935 | 0.934 | 0.17786 | 0.935 | 787.933 | 2 | 202 | <0.001 | 1.889 |
Table 4 represents the relationship of behavioural intention use behaviour of adopting e - pharmacy. The relationship with the independent variable (behavioural intention) with the dependent construct (use behaviour) is in the equation of:
| Model | Unstandardized Coefficient | Standardized Coefficient | t | Sig. | 95% Confidence Interval for B | Collinearity Statistics | |||
|---|---|---|---|---|---|---|---|---|---|
| B | Std. Error | Beta | Lower Bound | Upper Bound | Tolerance | VIF | |||
| Constant | 0.082 | 0.13 | 0.63 | 0.531 | -0.178 | 0.342 | |||
| BI | 0.984 | 0.035 | 0.967 | 28.07 | 0 | 0.914 | 1.054 | 1 | 1 |
Here, we can see a very strong significance of their relationship as the Beta Value of Behavioural Intention is 0.967. And also, the Sig. value is 0.00 (p>0.05) for BI, it can be stated that 4H0 is rejected and we can accept the Fourth alternative Hypothesis (4HA), stating “Behavioural Intention is having significant positive linear relationship with Use Behaviour in adopting E-pharmacy.”
RESULTS
The results of the hypothesis testing revealed that all four Null Hypotheses (H0) were rejected, suggesting that the factors under investigation significantly influence the adoption of e-pharmacy. Specifically, the rejection of H0 for Performance Expectancy indicates that how beneficial consumers perceive e-pharmacy to be in terms of improving their performance or convenience plays a crucial role in their intention to adopt the service. Similarly, the rejection of H0 for Effort Expectancy demonstrates that consumers' perceptions of how easy and convenient it is to use e-pharmacy significantly affect their willingness to adopt it. Moreover, the rejection of H0 for Trust highlights that trust defined as the belief in the security and reliability of e-pharmacy services is a key driver of consumers' behavioral intentions to adopt e-pharmacy. Finally, the rejection of H0 for Behavioral Intention and Use Behavior confirms that consumers' intention to use e-pharmacy is a strong predictor of their actual usage, meaning that a positive behavioral intention is likely to lead to actual adoption. In summary, these findings underline the importance of performance expectancy, effort expectancy, trust, and behavioral intention in the successful adoption and use of e-pharmacy services.
DISCUSSION
The purpose of this research is to scrutinize the driving factors of behavioural intention provoking use behaviour in adoption of e-pharmacy in Kolkata mainly concentrating on three constructs namely, Performance Expectancy (PE), Effort Expectancy (EE) and Trust (T) subsequently examining the effect of Behavioural Intention (BI) on Use Behaviour (UB). The findings of this empirical study indicated the rejection of all the four-null hypothesis, resulting the confirmation of significant positive linear relationships between PE and BI, EE and BI, Trust and BI, and finally BI and UB. These results merge broadly with the current literature of digital health and technology adoption, providing deep insights to rising e-pharmacy adoption scenario in India.
The outcome that Performance Expectancy significantly provokes Behavioural Intention states that from the perception of users the work friendly nature and value-added features of e-pharmacy platforms is mainly shaping their disposition to accept this technology into their daily life (Ezeudoka and Fan, 2024). On the other hand, a study found out that PE had no direct relationship with e-pharmacy adoption intention among the Indian consumers (Ahalawat et al., 2024).
The quantitative approach also inspects and finds out a significant positive relationship between Effort Expectancy and Behavioural Intention, stating that perceived simplicity of ease of use of the e-pharmacy applications can meaningfully shape the adoption decision. Our study is also reinforced by prior empirical research conducted in India, in 2020, published in the International Journal of Pharmaceutical and Healthcare Marketing, assured a positive connection of EE with the intention to adopt and promote online pharmacy services in India. Though, it must be considered that study by Yin et al., (2016) showed mixed outcomes, and also documenting a non-significant or indirect relationship of EE with BI. Reducing the perceived barrier to adopt e-pharmacy, the gravity of this study portrays improvised digital literacy in Kolkata, broader smartphone penetration, along with heightened closeness with digital health care services following the COVID-19 pandemic.
Among the three antecedents, Trust is considered as the strongest predictor of Behavioural Intention, focusing its crucial role in the digital healthcare sector, which is also braced by former research indicating that trust is identified as an utmost important factor driving the adoption of e-pharmacy services in India as it is directly connected to data security, authenticity of medicines, payment safety and reliability of service providers (Alsadoun et al., 2022). Therefore, to foster consumer confidence and promote adoption, the e-pharmacy operators should prioritise transparency, clear regulatory compliance, high quality assurance and protected technological support to achieve the trust of the consumers.
Lastly, this study also defines a significant positive relationship between Behavioural Intention and Use Behaviour, emphasising one of the main suppositions of UTAUT and TAM models. This fits with the studies in both surfacing and developed domains where BI is considered the strongest influence of actual usage. Though, it is necessary to recognize that the vast literature review represents mixed outcomes. Dwivedi et al., 2020 highlighted in their study that the BI-UB association is not globally constant among all the online services provided and may be influenced by random constraints, infrastructural obstructions or perceived risks. A place, like Kolkata, where electronic healthcare is seen to be emerging in recent years, as a result consumer adoption of online services has also expedited, these problems are not considered to be a major bottleneck while addressing the adoption.
By collaborating prior evidence with modern literature and our findings, the research serves that adoption of e-pharmacy applications or portals is a multi-collaborative system influenced by perceived usefulness, ease of use, and perceived trustworthiness, all of which leads into robust behavioural intention and eventual usage behaviour. These perceptions initiate consequential inference for healthcare administrators, online pharmacy operators and digital developers with the aim to upgrade the sustainability and penetration of e-pharmacy services in Kolkata, India.
CONCLUSION
Our research study set out to examine the constructs inferring consumers’ behaviour toward e-pharmacy services adoption in Kolkata by scrutinizing the effects of the factors namely, Performance Expectancy, Effort Expectancy and Trust on Behavioural Intention, and the consequent influence of Behavioural Intention on Use Behaviour. The outcomes define that all three independent antecedents significantly and positively influence behavioural intention, with Trust highlighted as the strongest prognosticator. This focuses the immense significance of perceived reliability, privacy and authenticity in captivating consumer adoption of mobile pharmacy services. It is noticed that Performance Expectancy and Effort Expectancy displayed insightful bond with behavioural intention, pointing that consumer are more disposed to adopt e-pharmacies when they perceive the applications as practical, functional, accessible and easy to use. Moreover, the central premise of leading technology adoption models is prominently validated by significant positive relationship between Behavioural Intention and Use Behaviour, assuring that intention converts successfully into real-life usage within the scenario of Kolkata’s emerging digital health hub.
The results accumulatively focus on e-pharmacy adoption being shaped by integration of rational, minimalist and trust-worthiness constructs, and they bolster the importance of UTAUT-based factors in defining the pattern of consumer behaviour in India’s rising healthcare ecosystem. As online healthcare services continue to extent, the understanding from this study offer insightful guidance for healthcare administrators, policymakers and e-pharmacy service providers planning to upgrade consumer engagement, strengthen service quality and promote wider scale reliability uptake of online pharmacy applications. Research in future may expand our study by investigating moderating demographic factors such as age, digital experience, area of living and socio-economic status of the consumers or by initiating longitudinal patterns to thoroughly acknowledge substitution in adoption behaviour over the period of time.