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This paper aims to identify and empirically validate the various factors for adoption, usage and intention to recommend e-pharmacy for purchasing medications by consumers.

Based on constructs from well-established theoretical models, the technology acceptance model, extended unified theory of acceptance and use of technology and self-determination theory, a model was proposed for the study. The model was validated with a sample size of 184 respondents using partial least squares method and factor analysis to establish and validate relationships among the various identified constructs.

The results show that performance expectancy, effort expectancy, social influence and hedonic motivation have a positive co-relation with adoption of e-pharmacy and the intention to recommend. The results depict that gender and educational background have no correlation toward adoption and intention to recommend e-pharmacies for purchasing medicines in India.

This research comes along with a geographic limitation of the sample size. The research was conducted in an urban suburb city of Bengaluru, India.

At an academic level, this research will provide interesting insights for exploring adoption and usage intention of consumers toward e-pharmacy. At a managerial level, this empirically supported study will provide insights into the relationship among the various constructs and the consumers’ motivation toward adoption and usage intention of e-pharmacy.

This research is the first of its form which uses constructs from the technology acceptance model, extended unified theory of acceptance and use of technology and self-determination theory in the online healthcare space to understand consumer usage behavior.

The purpose of this study is to present a large-scale real-world comparative study using pre-COVID lockdown data versus post-COVID lockdown data on predicting shipment times of therapeutic supplies in e-pharmacy supply chains and show that our proposed methodology is robust to lockdown effects.

The researchers used organic data of over 5.9 million records of therapeutic shipments, with 2.87 million records collected pre-COVID lockdown and 3.03 million records collected post-COVID lockdown. The researchers built various Machine Learning (ML) classifier models on the two datasets, namely, Random Forest (RF), Extra Trees (XRT), Decision Tree (DT), Multi-Layer Perceptron (MLP), XGBoost (XGB), CatBoost (CB), Linear Stochastic Gradient Descent (SGD) and the Linear Naïve Bayes (NB). Then, the researchers stacked these base models and built meta models on top of them. Further, the researchers performed a detailed comparison of the performances of ML models on pre-COVID lockdown and post-COVID lockdown datasets.

The proposed approach attains performance of 93.5% on real-world post-COVID lockdown data and 91.35% on real-world pre-COVID lockdown data. In contrast, the turn-around times (TAT) provided by therapeutic supply logistics providers are 62.91% accurate compared to reality in post-COVID lockdown times and 73.68% accurate compared to reality pre-COVID lockdown times. Hence, it is clear that while the TAT provided by logistics providers has deteriorated in the post-pandemic business climate, the proposed method is robust to handle pandemic lockdown effects on e-pharmacy supply chains.

The implication of the study provides a novel ML-based framework for predicting the shipment times of therapeutics, diagnostics and vaccines, and it is robust to COVID-19 lockdown effects.

E-pharmacy companies can readily adopt the proposed approach to enhance their supply chain management (SCM) capabilities and build resilience during COVID lockdown times.

The present study is one of the first to perform a large-scale real-world comparative analysis on predicting therapeutic supply shipment times in the e-pharmacy supply chain with novel ML ensemble stacking, obtaining robust results in these COVID lockdown times.

This paper aims to address the pressing problem of prediction concerning shipment times of therapeutics, diagnostics and vaccines during the ongoing COVID-19 pandemic using a novel artificial intelligence (AI) and machine learning (ML) approach.

The present study used organic real-world therapeutic supplies data of over 3 million shipments collected during the COVID-19 pandemic through a large real-world e-pharmacy. The researchers built various ML multiclass classification models, namely, random forest (RF), extra trees (XRT), decision tree (DT), multilayer perceptron (MLP), XGBoost (XGB), CatBoost (CB), linear stochastic gradient descent (SGD) and the linear Naïve Bayes (NB) and trained them on striped datasets of (source, destination, shipper) triplets. The study stacked the base models and built stacked meta-models. Subsequently, the researchers built a model zoo with a combination of the base models and stacked meta-models trained on these striped datasets. The study used 10-fold cross-validation (CV) for performance evaluation.

The findings reveal that the turn-around-time provided by therapeutic supply logistics providers is only 62.91% accurate when compared to reality. In contrast, the solution provided in this study is up to 93.5% accurate compared to reality, resulting in up to 48.62% improvement, with a clear trend of more historic data and better performance growing each week.

The implication of the study has shown the efficacy of ML model zoo with a combination of base models and stacked meta-models trained on striped datasets of (source, destination and shipper) triplets for predicting the shipment times of therapeutics, diagnostics and vaccines in the e-pharmacy supply chain.

The novelty of the study is on the real-world e-pharmacy supply chain under post-COVID-19 lockdown conditions and has come up with a novel ML ensemble stacking based model zoo to make predictions on the shipment times of therapeutics. Through this work, it is assumed that there will be greater adoption of AI and ML techniques in shipment time prediction of therapeutics in the logistics industry in the pandemic situations.

Up until now, some internet shopper profiles based on certain findings have been generally accepted. It is widely believed that internet shoppers tend to be more innovative, less averse to risk, more variety-seeking, more information-seeking, more impulsive and more interested in pursuing convenience. This paper aims to discuss some of these assumed characteristics by contrasting and comparing customers of three different types of pharmacies.

Three exploratory-descriptive design studies are conducted to profile customers from three different types of pharmacies in Mexico. Data from three samples are assessed, including 198 customers of physical drugstores without an e-channel, 150 customers of physical pharmacies with an e-channel and 271 customers of online pharmacies.

Shoppers from physical pharmacies purchase more food and drinks than actual medicine. Shoppers from physical pharmacies with e-channels tend to use the internet to acquire information about pharmaceutical products but do not make purchases online; they prefer to obtain products immediately from a physical drugstore instead of waiting for delivery from an e-channel. Contrasting with the two former customer types, shoppers who use e-pharmacies are specific in the numbers and types of products they purchase, medicines being the main priority.

The three types of customers and their preferred shopping platforms may show important profile differences. Despite the evidence in previous literature, shoppers at physical pharmacies are not necessarily non-convenience oriented (time and effort), noninformation analyzing, non-price conscious or less positive in their attitude about shopping; instead, they may simply be averse to technology.

The purpose of this research is to examine the potential of e‐health by focusing explicitly on the delivery of health care products and services. The examination of e‐health activity is guided by one broad research question, “What is the potential for constructing e‐health strategy as an innovative health technology?”. A great amount of attention has been given to e‐health activity in the present day. However important this form of e‐health is, this type of service simply does not face the same constraints that must be addressed by those actually delivering health care services.

The researchers employed a qualitative data collection technique to formulate more examples and cases to derive lessons for Jordan. Phone interviews in a random sample were conducted with corporate officers in Jordan in order to reveal the internal organizational structure and business trends, interface issues, marketing strategies, as well as comparing and contrasting the online health world to the traditional health care realm.

Internet‐related projects is a top priority for health care information technology executives in the present day, with a cautious approach toward “e‐health”, as many products have yet to mature, and that the “click and mortar” model may perhaps be the optimal strategy for e‐health in Jordan.

This paper reviews the e‐health trends to demonstrate the tremendous potential for health‐related commercial activity on the internet. However, the researcher examining the barriers facing e‐health to the Jordanian health system also pointed out almost insurmountable challenges.

Despite the apparent promise of e‐health, its instability is measured by its failure so far to systematically penetrate the organization of health care. Beyond the pragmatic negotiation of e‐health in the immediate context of clinical practice, there are wider issues about how the development/implementation of e‐health is funded, about its organization and management at the policy level; and about its potential medico‐legal risks.

It is hoped that the handful of ventures into cyber medicine appears to be coming from a few enterprising physicians who have set up medical practices on the Web.

This study aims to understand the determinants of online pharmacy or epharmacy adoption among young consumers in Bangladesh using an extended unified theory of acceptance and use of technology (UTAUT) model.

A structured Google Docs questionnaire was sent out to 420 respondents using messenger service; 285 useable responses were finally extracted. Data were empirically validated using the two-staged structural equation model (SEM)-neural network analysis approach.

The robustness of the classical UTAUT model remains intact in the context of online pharmacy adoption. Among the integrated variables, while perceived trust and health literacy were found significant, perceived risk and personal innovativeness were found insignificant in determining consumers’ intention to adopt online pharmacy. The neural network analysis provided further verification of these findings derived from the SEM.

The findings of this study would facilitate in devising better strategies for entering or expanding online pharmacy business in developing countries such as Bangladesh.

The originality of the current study relates to the two-fold contributions of this study. First, while this study extended the classical UTAUT model by incorporating perceived risk, perceived trust, personal innovativeness and health literacy, the inclusion of the following two variables is fresh within the extant online pharmacy literature. Second, by using a two-staged SEM-neural network analysis approach, this study advances the past studies on e-commerce adoption in pharmaceutical settings and provides a general understanding of the customers of developing countries.

The purpose of this study is to identify antecedents of adoption and post-adoption switching of online pharmacy applications (OPA) in Indian society. A push-pull-mooring (PPM) model was formulated to evaluate the impact of various constructs upon “consumers’ switching intention” (CSI).

An online questionnaire was sent to 252 users of OPA in India. Hypotheses were generated to examine the push, pull and mooring effects of constructs developed. The relationships between dependent and independent variables were evaluated using structured equation modeling (SEM).

The study explicated the effect of PPM constructs on CSI in the context of OPA adoption. “Perceived usefulness,” “perceived ease of use” and “alternative attractiveness” had a significant “pull” effect on CSI. “Switching cost” had a “mooring” effect on CSI, whereas the degree of “customer involvement in decision-making” was found to have a “push” effect upon CSI.

This study theoretically established that the constructs of “perceived usefulness,” “perceived ease of use” and “alternative attractiveness” had significant “pull” effect on “consumers’ switching intention.” The construct of “switching cost” had a “mooring” effect on CSI, whereas the degree of “customer involvement in decision-making” was found to have a “push” effect upon CSI.

The study provided valuable insights regarding consumer behavior regarding OPAs. These findings could be applied by managers in framing effective strategies to grow and retain the customer base of OPAs.

To the best of the authors’ knowledge, this was one of the first empirical investigative studies to assess precursors of adoption and post-adoption characteristics of consumer behavior through the PPM model, in the context of Indian OPAs.

The purpose of this paper is to elucidate the pathways that can enhance pharmacy‐to‐customer engagements, and give capacity to build closely aligned customer interface systems.

A nationwide, pharmacy and customer, dual survey‐based service value networks (SVNs) approach, analysed using structural equation modelling (SEM), shows significant business‐customer encounter information pathways act between the pharmacy and its engaging customer.

The complex nature of the business‐customer exchange and its interacting pathways is highlighted. Six front‐end SVNs business cells engaged in this paper have significant direct (and/or indirect) impact on customer perspectives of their pharmacy. Hence, the pharmacy front‐end business model should be fully and intelligently networked.

The SVNs and SEM approach yields a strong robust pharmacy model, and can move pharmacy business management mechanisms to elevated customer‐engaging levels. It can offer customer‐targeted interaction solutions with enhanced perceived satisfaction. This SVNs approach is efficient, understandable, measurable and business specific. It is appropriate for market leaders, innovators or differentiators. Combined with other interface‐related toolkits it can deliver competitive advantage parameters such as understanding the key measures that improve business‐customer alignment.

SVNs developed under SEM offer a new way to better align the business with its customers. They can be applied at the individual community pharmacy or pharmacy chain level. SVNs release the key measures from which pertinent interacting front‐end business‐to‐customer pathways may be adjusted in a quest to strategically build and align the business closely to its customer demands. This win‐win SVNs interface procedure can also be applied within other service industries.

No one knows what the future holds. Not least for substance use and addictions. Few predicted the psychedelic movement in the 1960s, the crack ‘epidemic’ in the 1980s and the ‘E’ generation of the 1990s ‐ all of which had a profound influence on our culture, youth and our health. So what of the Naughties, Teenies and Twenties? With increased globalisation, new technologies, increasing spending power and the scope for increased pleasure‐seeking we are destined for more and new addictions. In this groundbreaking article, Jane Mounteney applies the technique of scenario planning to investigate a future dominated by technology, smart and nanodrugs and an ever‐increasing availability of drugs. With the emergence of the super nerd and groovy geek, who will be there to help the fallen?