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This study aims to present a comprehensive investigation into users’ behavioural intentions to use e-pharmacies through the lens of the privacy calculus model. The present study also investigates the effects of perceived benefit, perceived privacy risk, timeliness and perceived app quality on e-pharmacy usage through the mediating role of trustworthiness.

The study used a deductive approach and collected data from 338 respondents using the purposive sampling technique. partial least squares structural equation modelling was applied to analyse the data.

The findings of the study indicate that perceived benefit, perceived privacy risk, timeliness and perceived app quality do not directly impact users’ behavioural intentions towards e-pharmacy adoption. Instead, it demonstrated that perceived benefit, perceived privacy risk, timeliness and perceived app quality influenced behavioural intention indirectly through the mediating role of trustworthiness

This study offers valuable insights to entrepreneurs, marketers and policymakers, enabling them to develop regulations, guidelines and policies that cultivate trust, safeguard privacy, ensure prompt services and create an enabling environment for the adoption of e-pharmacies. The present study also contributes to the existing literature by extending the privacy calculus model with the integration of timeliness and perceived app quality to explain users’ adoption behaviour towards e-pharmacy.

This study aims to examine the mediating role of social influence on the relationship between key predictors of E-pharmacy adoption among young consumers based on the unified theory of adoption and use of technology (UTAUT).

This study employs a quantitative correlational research design. Based on cluster sampling, data was collected from 306 university students from three public universities in southwestern Nigeria. Data was analysed using partial least square structural equation modeling.

The primary determinant driving the adoption of e-pharmacy is performance expectancy. Social influence plays a partial mediating role in linking performance expectancy to e-pharmacy adoption. In contrast, it fully mediates the relationship between effort expectancy, facilitating conditions and the adoption of e-pharmacy services.

This study provides theoretical clarity on recent issues within the UTAUT framework. Findings highlight the complexity of how social factors interact with individual beliefs and external conditions in determining technology acceptance.

Research includes information relevant to access the impact of e-pharmacy services on healthcare accessibility, affordability and quality in developing countries.

The findings extend the adoption of technology literature in healthcare and offer a new understanding of adoption dynamics. The results emphasize the importance of performance expectancy in driving e-pharmacy adoption, providing a clear direction for stakeholders to enhance service quality and user experience of e-pharmacy. Additionally, the mediating effect of social influence highlights the significance of peer recommendations, celebrity endorsements and social media campaigns in shaping consumer adoption of e-pharmacies among young people.

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 study aims to investigate the factors influencing the adoption of online pharmacies in Qatar using the unified theory of acceptance and use of technology-2 (UTAUT-2) framework. Specifically, this study examines the impact of performance expectancy, effort expectancy, social influence, hedonic motivation, habit, technology trust, perceived risk and users’ level of awareness of behavioral intention, which in turn affects the adoption of online pharmacies. Furthermore, this study explores the moderating role of word-of-mouth (WOM) recommendations on the relationship between behavioral intention and online pharmacy adaptation.

This study adopted a descriptive, quantitative approach to investigate the UTAUT-2 model in the context of consumers’ adoption of e-pharmacy in Qatar. Through convenience sampling, 455 responses were collected from regular customers accessing online pharmacy services. The data were analyzed using Smart-PLS 3.2 software to examine the hypothesized relationships.

The results showed that WOM recommendations significantly enhanced the relationship between behavioral intention and adopting online pharmacies in Qatar. This study identified the factors that may hinder or enable the adoption of online pharmacies, including performance expectancy, effort expectancy, social influence, hedonic motivation, habit, technology trust, perceived risk and users’ level of awareness.

This study contributes to the existing literature on technology acceptance by extending the UTAUT-2 model and recognizing three additional variables (perceived risk, technology trust and technology awareness). These need to be investigated against UTAUT-2 variables to detect the significance of their impact on adapting the e-health concept in Qatar. The potential for cultural change to accelerate the adoption of online pharmacies is highlighted. Future research should explore the role of moral and cultural factors in technology adoption.

The results underscore the economic and social significance of e-pharmacy adoption, particularly within the context of a developing country. Considering the positive intentions expressed by individuals toward e-pharmacy, it becomes crucial for managers and decision-makers to make strategic choices to address any challenges that may arise. Policymakers are encouraged to enhance their services and implement various development initiatives to expand e-pharmacy accessibility and availability.

This study builds upon previous research on e-commerce in the pharmaceutical industry and provides a comprehensive understanding of customers in developing countries. Extending the UTAUT-2 model and identifying additional variables contributes to the knowledge of e-health concepts in Qatar. The findings have practical implications for developing strategies to promote online pharmacy adoption in Qatar and other countries.

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.

This case study would enable students to understand the concept, process and advantages of mergers and acquisitions as a growth strategy with respect to 1mg. Also, the students would be able to use the threats, opportunities, weaknesses and strengths matrix to map 1mg’s strengths, weaknesses, opportunities and threats.

This case study analyses the transformation journey of 1mg to Tata 1mg, one of the most trusted internet pharmacies in India. This case describes a small start-up that was launched in 2013 and had made many acquisitions since then. This case revolves around Tata Digital’s purchase of 1mg. The case starts out by explaining 1mg’s financial situation and why the company was acquired. This case study focuses on how the integration helped Tata Digital and 1mg realize their respective missions. Furthermore, the case study illustrates the benefits and difficulties of this integration.

This case study is basically aimed at postgraduate management students; it can be used in strategic management and health-care courses. Students can understand the concept of diversification and acquisition with the help of this case study. Students can also gain an insight into the organic and inorganic diversification as a growth strategy.

Teaching notes are available for educators only.

CSS 11: Strategy.

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.