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While attempting to persuade surgeons to accept their health technology, sales representatives for medical devices face daily challenges in the operating room. Surgeons exhibit cognitive complexity (abstractness vs. concreteness) when accepting any form of health technology. Surgeons choose technologies on behalf of their patients, taking patient priorities and expectations into account. Prior research has focused on cognitive complexity in the context of health technology adoption, but the issue of technology acceptance has not been addressed. The purpose of this study to use the construal level (CL) theory to determine the role of behavioural abstraction levels in the acceptance of surgical health technology.

On the basis of 556 min of seminar-based data and semi-directive interviews, the surgeons’ experiences regarding the acceptance of health technology were analysed. A non-directive observational method was used to permit the spontaneous emergence of CL dimensions in a natural environment. A categorization model was used for data coding, and MAXQDA, in addition to traditional multidimensional scaling and hierarchical cluster analysis, was used to generate results with joint displays.

Effort expectancy, learning curve, performance risk, habit, patient clinical condition, clinical outcome expectancy, technology setting and social influence were construed at a low construal level (LCL). On the other hand, patient paying capacity, technology cost, price value, financial risk and patient performance expectation were construed at a high construal level (HCL). The study also reveals duality-based factors which showed proximity to HCL but intersected at LCL, and vice versa. Duality-based factors such as effort expectancy, surgical technique, trust and perceived risk intersected at HCL, whereas performance expectancy, relative advantage, time expectancy, perceived value, physical risk and peer group influence intersected at LCL.

This is one of the early studies that presented the impact of behavioural abstraction on behavioural intention to accept health technology for surgeries.

Allocation of the marketing budget has become increasingly challenging due to the diverse channel exposure to customers. This study aims to enhance global marketing knowledge by introducing an ensemble attribution model to optimize marketing budget allocation for online marketing channels. As empirical research, this study demonstrates the supremacy of the ensemble model over standalone models.

The transactional data set for car insurance from an Indian insurance aggregator is used in this empirical study. The data set contains information from more than three million platform visitors. A robust ensemble model is created by combining results from two probabilistic models, namely, the Markov chain model and the Shapley value. These results are compared and validated with heuristic models. Also, the performances of online marketing channels and attribution models are evaluated based on the devices used (i.e. desktop vs mobile).

Channel importance charts for desktop and mobile devices are analyzed to understand the top contributing online marketing channels. Customer relationship management-emailers and Google cost per click a paid advertising is identified as the top two marketing channels for desktop and mobile channels. The research reveals that ensemble model accuracy is better than the standalone model, that is, the Markov chain model and the Shapley value.

To the best of the authors’ knowledge, the current research is the first of its kind to introduce ensemble modeling for solving attribution problems in online marketing. A comparison with heuristic models using different devices (desktop and mobile) offers insights into the results with heuristic models.