Search results

This study is to propose a procedure to support decisions on which enablers should be employed to minimize the impact of barriers to implementing mass customization strategies in food companies.

Through interpretive structural modeling, the authors analyzed the relationships between barriers. Then, with an approach similar to the quality function deployment technique, commonly used in general product and process development, the authors clarified the relationships between barriers and enablers.

The results revealed 19 barriers and 17 enablers for implementing food mass customization. The analysis indicates that most of the barriers (16) present strong associations with each other. The barrier “products with non-customizable features” depends on the whole chain of associations and causes a minor impact on the other barriers. In turn, the barrier “ingredient incompatibility” causes impact over the whole chain, and its dependence on other barriers is very low.

The results were tested in a single Brazilian company in the food sector.

The findings can allow food manufacturing companies to focus their efforts on the improvement of enabling technologies, such as smart packaging, Internet of Things and additive manufacture.

This study would help food companies to improve their business and provide better products to society.

There are few recommendations in the literature to how to implement mass customization strategy in companies from the food sector. This study fills in this gap presenting a procedure to guide managerial staff to develop this promising approach for food companies.

The evolution of e-health technologies presents promising alternatives for health-care excellence. Despite the benefits arising from mobile e-health (m-health) and wearables technologies, the literature stands many contradictories signs regarding how users accept and engage in using these technologies. This study aims to synthesize the estimations about m-health user acceptance technologies.

A meta-analytic structural equation modeling was carried out using the 778 relationships estimated by 100 previous research. The estimations follow the relations and constructs proposed in the UTAUT2 technological acceptance model.

The results indicate the performance expectancy, effort expectancy, social influence and habit constructs are most important for predicting the behavioral intention of use of m-health technologies. The Latin American users of e-health technologies are still underestimated in the literature.

The study presents a guide to understanding the acceptance process of m-health technologies and delivers a general orientation for developing new m-health devices considering their acceptance by users.